{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecaster explainability: Feature importance, SHAP Values and Partial Dependence Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine learning explainability, also known as interpretability, refers to the ability to understand, interpret, and explain the decisions or predictions made by machine learning models in a human-understandable way. It aims to shed light on how a model arrives at a particular result or decision.\n",
    "\n",
    "Due to the complex nature of many modern machine learning models, such as ensemble methods, they often function as black boxes, making it difficult to understand why a particular prediction was made. Explainability techniques aim to demystify these models, providing insight into their inner workings and helping to build trust, improve transparency, and meet regulatory requirements in various domains. Enhancing model explainability not only aids in understanding model behavior but also helps detect biases, improve model performance, and enables stakeholders to make more informed decisions based on machine learning insights.\n",
    "\n",
    "skforecast is compatible with some of the most used interpretability methods: Shap values, Permutation importance, Partial Dependency Plots and Model-specific methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "To learn more about explainability, visit: <a href=\"https://cienciadedatos.net/documentos/py57-interpretable-forecasting-models.html\">Interpretable forecasting models</a>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import shap\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.inspection import PartialDependenceDisplay\n",
    "from lightgbm import LGBMRegressor\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.model_selection import TimeSeriesFold, backtesting_forecaster\n",
    "from skforecast.plot import set_dark_theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────── <span style=\"font-weight: bold\">vic_electricity</span> ─────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                             │\n",
       "│ Half-hourly electricity demand for Victoria, Australia                   │\n",
       "│                                                                          │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                  │\n",
       "│ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse │\n",
       "│ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/,              │\n",
       "│ https://github.com/tidyverts/tsibbledata/.                               │\n",
       "│ https://tsibbledata.tidyverts.org/reference/vic_elec.html                │\n",
       "│                                                                          │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                     │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                 │\n",
       "│ datasets/main/data/vic_electricity.csv                                   │\n",
       "│                                                                          │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 52608 rows x 4 columns                                            │\n",
       "╰──────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────── \u001b[1mvic_electricity\u001b[0m ─────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                             │\n",
       "│ Half-hourly electricity demand for Victoria, Australia                   │\n",
       "│                                                                          │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                  │\n",
       "│ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse │\n",
       "│ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/,              │\n",
       "│ https://github.com/tidyverts/tsibbledata/.                               │\n",
       "│ https://tsibbledata.tidyverts.org/reference/vic_elec.html                │\n",
       "│                                                                          │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                     │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                 │\n",
       "│ datasets/main/data/vic_electricity.csv                                   │\n",
       "│                                                                          │\n",
       "│ \u001b[1mShape:\u001b[0m 52608 rows x 4 columns                                            │\n",
       "╰──────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Demand</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Date</th>\n",
       "      <th>Holiday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-12-31 13:00:00</th>\n",
       "      <td>4382.825174</td>\n",
       "      <td>21.40</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31 13:30:00</th>\n",
       "      <td>4263.365526</td>\n",
       "      <td>21.05</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31 14:00:00</th>\n",
       "      <td>4048.966046</td>\n",
       "      <td>20.70</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Demand  Temperature        Date  Holiday\n",
       "Time                                                              \n",
       "2011-12-31 13:00:00  4382.825174        21.40  2012-01-01     True\n",
       "2011-12-31 13:30:00  4263.365526        21.05  2012-01-01     True\n",
       "2011-12-31 14:00:00  4048.966046        20.70  2012-01-01     True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name=\"vic_electricity\")\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Demand</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>82531.745918</td>\n",
       "      <td>21.047727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>227778.257304</td>\n",
       "      <td>26.578125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>275490.988882</td>\n",
       "      <td>31.751042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Demand  Temperature\n",
       "Time                                  \n",
       "2011-12-31   82531.745918    21.047727\n",
       "2012-01-01  227778.257304    26.578125\n",
       "2012-01-02  275490.988882    31.751042"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregation to daily frequency\n",
    "# ==============================================================================\n",
    "data = data.resample('D').agg({'Demand': 'sum', 'Temperature': 'mean'})\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split train-test\n",
    "# ==============================================================================\n",
    "data_train = data.loc[: '2014-12-21']\n",
    "data_test = data.loc['2014-12-22':]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "A forecasting model is created to predict the energy demand using the past 7 values (last week) and the temperature as an exogenous variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    \n",
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       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursive</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [1 2 3 4 5 6 7]</li>\n",
       "                    <li><strong>Window features:</strong> None</li>\n",
       "                    <li><strong>Window size:</strong> 7</li>\n",
       "                    <li><strong>Series name:</strong> Demand</li>\n",
       "                    <li><strong>Exogenous included:</strong> True</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 15:01:44</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 15:01:46</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    Temperature\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('2011-12-31 00:00:00'), Timestamp('2014-12-21 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <Day></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursive.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/autoregressive-forecaster.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=================== \n",
       "ForecasterRecursive \n",
       "=================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [1 2 3 4 5 6 7] \n",
       "Window features: None \n",
       "Window size: 7 \n",
       "Series name: Demand \n",
       "Exogenous included: True \n",
       "Exogenous names: Temperature \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Differentiation order: None \n",
       "Training range: [Timestamp('2011-12-31 00:00:00'), Timestamp('2014-12-21 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <Day> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0,\n",
       "    'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 15:01:44 \n",
       "Last fit date: 2025-11-26 15:01:46 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a recursive multi-step forecaster (ForecasterRecursive)\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags      = 7\n",
    "             )\n",
    "\n",
    "forecaster.fit(\n",
    "    y    = data_train['Demand'],\n",
    "    exog = data_train['Temperature']\n",
    ")\n",
    "forecaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-specific feature importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature importance is a technique used in machine learning to determine the relevance or importance of each feature (or variable) in a model's prediction. In other words, it measures how much each feature contributes to the model's output.\n",
    "\n",
    "Feature importance can be used for several purposes, such as identifying the most relevant features for a given prediction, understanding the behavior of a model, and selecting the best set of features for a given task. It can also help to identify potential biases or errors in the data used to train the model. It is important to note that feature importance is not a definitive measure of causality. Just because a feature is identified as important does not necessarily mean that it causes the outcome. Other factors, such as confounding variables, may also be at play.\n",
    "\n",
    "The method used to calculate feature importance may vary depending on the type of machine learning model being used. Different machine learning models may have different assumptions and characteristics that affect the calculation of feature importance. For example, decision tree-based models such as Random Forest and Gradient Boosting typically use mean decrease impurity or permutation feature importance methods to calculate feature importance. \n",
    "\n",
    "Linear regression models typically use coefficients or standardized coefficients to determine the importance of a feature. The magnitude of the coefficient reflects the strength and direction of the relationship between the feature and the target variable.\n",
    "\n",
    "The importance of the predictors included in a forecaster can be obtained using the method `get_feature_importances()`. This method accesses the `coef_` and `feature_importances_` attributes of the internal estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "The <code>get_feature_importances()</code> method will only return values if the forecaster's estimator has either the <code>coef_</code> or <code>feature_importances_</code> attribute, which is the default in scikit-learn. If your estimator does not follow this naming convention, please consider opening an [issue on GitHub](https://github.com/skforecast/skforecast/issues) and we will strive to include it in future updates.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Temperature</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lag_1</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lag_3</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lag_2</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lag_7</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lag_6</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lag_5</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lag_4</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       feature  importance\n",
       "7  Temperature         570\n",
       "0        lag_1         470\n",
       "2        lag_3         387\n",
       "1        lag_2         362\n",
       "6        lag_7         325\n",
       "5        lag_6         313\n",
       "4        lag_5         298\n",
       "3        lag_4         275"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predictors importances\n",
    "# ==============================================================================\n",
    "forecaster.get_feature_importances()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To properly retrieve the feature importances in the <code>ForecasterDirect</code> and <code>ForecasterDirectMultiVariate</code>, it is essential to specify the model from which to extract the feature importances are to be extracted. This is because [Direct Strategy Forecasters](../user_guides/direct-multi-step-forecasting.html) fit one model per step, and each model may have different important features. Therefore, the user must explicitly specify which model's feature importances wish to extract to ensure that the correct features are used."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SHAP explanations for skforecast models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SHAP (SHapley Additive exPlanations)** values are a widely adopted method for explaining machine learning models. They provide both visual and quantitative insights into how features and their values impact the model. SHAP values serve two primary purposes:\n",
    "\n",
    "+ **Global Interpretability**: SHAP values help identify how each feature influenced the model during training. By averaging SHAP values across the dataset, one can rank features by their overall importance and gain insight into the model’s decision-making process.\n",
    "\n",
    "+ **Local Interpretability**: SHAP values also explain individual predictions by indicating how much each feature contributed to a specific output. This enables a breakdown of single predictions to understand the role each feature played in the outcome.\n",
    "\n",
    "SHAP value explanations can be generated for skforecast models using two essential components:\n",
    "\n",
    "+ The internal estimator of the forecaster, accessible via `forecaster.estimator`.\n",
    "\n",
    "+ The internal matrices used for fitting, backtesting, and predicting with the forecaster. These matrices are accessible through the methods `create_train_X_y()` and `create_predict_X()`, and by setting the argument `return_predictors = True` in the `backtesting_forecaster()` function.\n",
    "\n",
    "By leveraging these elements, users can produce clear and interpretable explanations for their forecasting models. These explanations can be used to assess model reliability, identify the most influential features, and better understand the relationships between input variables and the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SHAP feature importance in the overall model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Averaging the shap values across the data set used to train the model, it is possible to obtain an estimation of the contribution (magnitude and direction) of each feature in the model. The higher the absolute value of the SHAP value, the more important the feature is for the model. The sign of the SHAP value indicates whether the feature has a positive or negative impact on the prediction.\n",
    "\n",
    "First, the training matrices used to fit the model are created with the method `create_train_X_y()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</th>\n",
       "      <th>lag_7</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-07</th>\n",
       "      <td>205338.714620</td>\n",
       "      <td>211066.426550</td>\n",
       "      <td>213792.376946</td>\n",
       "      <td>258955.329422</td>\n",
       "      <td>275490.988882</td>\n",
       "      <td>227778.257304</td>\n",
       "      <td>82531.745918</td>\n",
       "      <td>24.098958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-08</th>\n",
       "      <td>200693.270298</td>\n",
       "      <td>205338.714620</td>\n",
       "      <td>211066.426550</td>\n",
       "      <td>213792.376946</td>\n",
       "      <td>258955.329422</td>\n",
       "      <td>275490.988882</td>\n",
       "      <td>227778.257304</td>\n",
       "      <td>20.223958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>200061.614738</td>\n",
       "      <td>200693.270298</td>\n",
       "      <td>205338.714620</td>\n",
       "      <td>211066.426550</td>\n",
       "      <td>213792.376946</td>\n",
       "      <td>258955.329422</td>\n",
       "      <td>275490.988882</td>\n",
       "      <td>19.161458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    lag_1          lag_2          lag_3          lag_4  \\\n",
       "Time                                                                     \n",
       "2012-01-07  205338.714620  211066.426550  213792.376946  258955.329422   \n",
       "2012-01-08  200693.270298  205338.714620  211066.426550  213792.376946   \n",
       "2012-01-09  200061.614738  200693.270298  205338.714620  211066.426550   \n",
       "\n",
       "                    lag_5          lag_6          lag_7  Temperature  \n",
       "Time                                                                  \n",
       "2012-01-07  275490.988882  227778.257304   82531.745918    24.098958  \n",
       "2012-01-08  258955.329422  275490.988882  227778.257304    20.223958  \n",
       "2012-01-09  213792.376946  258955.329422  275490.988882    19.161458  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Time\n",
       "2012-01-07    200693.270298\n",
       "2012-01-08    200061.614738\n",
       "2012-01-09    216201.836844\n",
       "Freq: D, Name: y, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training matrices used by the forecaster to fit the internal estimator\n",
    "# ==============================================================================\n",
    "X_train, y_train = forecaster.create_train_X_y(\n",
    "                       y    = data_train['Demand'],\n",
    "                       exog = data_train['Temperature']\n",
    "                   )\n",
    "\n",
    "display(X_train.head(3))  # Features\n",
    "display(y_train.head(3))  # Target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, the SHAP values are calculated using the `shap` library. The `shap_values()` method is used to calculate the SHAP values for the training data. If the data set is large, it is recommended to use only a random sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create SHAP explainer (for three base models)\n",
    "# ==============================================================================\n",
    "explainer = shap.TreeExplainer(forecaster.estimator)\n",
    "\n",
    "# Sample 50% of the data to speed up the calculation\n",
    "rng = np.random.default_rng(seed=785412)\n",
    "sample = rng.choice(X_train.index, size=int(len(X_train) * 0.5), replace=False)\n",
    "X_train_sample = X_train.loc[sample, :]\n",
    "shap_values = explainer.shap_values(X_train_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the SHAP values are calculated, several plots can be generated to visualize the results."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP Summary Plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The SHAP summary plot typically displays the feature importance or contribution of each feature to the model's output across multiple data points. It shows how much each feature contributes to pushing the model's prediction away from a base value (often the model's average prediction). By examining a SHAP summary plot, one can gain insights into which features have the most significant impact on predictions, whether they positively or negatively influence the outcome, and how different feature values contribute to specific predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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Tu=!1,ia&&1048576&t.flags&&ea(t,Qr,t.index);switch(t.lanes=0,t.tag){case 16:e:{e=t.pendingProps;var r=t.elementType,a=r._init;if(r=a(r._payload),t.type=r,\"function\"!=typeof r){if(null!=r){if((a=r.$$typeof)===k){t.tag=11,t=zu(null,t,r,e,n);break e}if(a===E){t.tag=14,t=Mu(null,t,r,e,n);break e}}throw t=A(r)||r,Error(o(306,t,\"\"))}Rr(r)?(e=vu(r,e),t.tag=1,t=Ru(null,t,r,e,n)):(t.tag=0,t=Fu(null,t,r,e,n))}return t;case 0:return Fu(e,t,t.type,t.pendingProps,n);case 1:return Ru(e,t,r=t.type,a=vu(r,t.pendingProps),n);case 3:e:{if(q(t,t.stateNode.containerInfo),null===e)throw Error(o(387));r=t.pendingProps;var i=t.memoizedState;a=i.element,ri(e,t),ci(t,r,null,n);var u=t.memoizedState;if(r=u.cache,ba(0,Aa,r),r!==i.cache&&ka(t,[Aa],n,!0),si(),r=u.element,i.isDehydrated){if(i={element:r,isDehydrated:!1,cache:u.cache},t.updateQueue.baseState=i,t.memoizedState=i,256&t.flags){t=ju(e,t,r,n);break e}if(r!==a){ga(a=Sr(Error(o(424)),t)),t=ju(e,t,r,n);break 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null}(r,t.type,t.pendingProps,ua))?(t.stateNode=r,ra=t,aa=yf(r.firstChild),ua=!1,a=!0):a=!1),a||sa(t)),Y(t),a=t.type,i=t.pendingProps,u=null!==e?e.memoizedProps:null,r=i.children,uf(a,i)?r=null:null!==u&&uf(a,u)&&(t.flags|=32),null!==t.memoizedState&&(a=Ni(e,t,Li,null,null,n),Gf._currentValue=a),Lu(e,t),Pu(e,t,r,n),t.child;case 6:return null===e&&ia&&((e=n=aa)&&(null!==(n=function(e,t,n){if(\"\"===t)return null;for(;3!==e.nodeType;){if((1!==e.nodeType||\"INPUT\"!==e.nodeName||\"hidden\"!==e.type)&&!n)return null;if(null===(e=yf(e.nextSibling)))return null}return e}(n,t.pendingProps,ua))?(t.stateNode=n,ra=t,aa=null,e=!0):e=!1),e||sa(t)),null;case 13:return Bu(e,t,n);case 4:return q(t,t.stateNode.containerInfo),r=t.pendingProps,null===e?t.child=nu(t,null,r,n):Pu(e,t,r,n),t.child;case 11:return zu(e,t,t.type,t.pendingProps,n);case 7:return Pu(e,t,t.pendingProps,n),t.child;case 8:case 12:return Pu(e,t,t.pendingProps.children,n),t.child;case 10:return r=t.pendingProps,ba(0,t.type,r.value),Pu(e,t,r.children,n),t.child;case 9:return a=t.type._context,r=t.pendingProps.children,Ea(t),r=r(a=Ca(a)),t.flags|=1,Pu(e,t,r,n),t.child;case 14:return Mu(e,t,t.type,t.pendingProps,n);case 15:return Nu(e,t,t.type,t.pendingProps,n);case 19:return Yu(e,t,n);case 31:return r=t.pendingProps,n=t.mode,r={mode:r.mode,children:r.children},null===e?((n=Wu(r,n)).ref=t.ref,t.child=n,n.return=t,t=n):((n=jr(e.child,r)).ref=t.ref,t.child=n,n.return=t,t=n),t;case 22:return Au(e,t,n);case 24:return Ea(t),r=Ca(Aa),null===e?(null===(a=Ba())&&(a=rs,i=Oa(),a.pooledCache=i,i.refCount++,null!==i&&(a.pooledCacheLanes|=n),a=i),t.memoizedState={parent:r,cache:a},ni(t),ba(0,Aa,a)):(!!(e.lanes&n)&&(ri(e,t),ci(t,null,null,n),si()),a=e.memoizedState,i=t.memoizedState,a.parent!==r?(a={parent:r,cache:r},t.memoizedState=a,0===t.lanes&&(t.memoizedState=t.updateQueue.baseState=a),ba(0,Aa,r)):(r=i.cache,ba(0,Aa,r),r!==a.cache&&ka(t,[Aa],n,!0))),Pu(e,t,t.pendingProps.children,n),t.child;case 29:throw t.pendingProps}throw Error(o(156,t.tag))}function Zu(e){e.flags|=4}function Ju(e,t){if(\"stylesheet\"!==t.type||4&t.state.loading)e.flags&=-16777217;else if(e.flags|=16777216,!Bf(t)){if(null!==(t=au.current)&&((4194048&is)===is?null!==iu:(62914560&is)!==is&&!(536870912&is)||t!==iu))throw Za=Ya,qa;e.flags|=8192}}function el(e,t){null!==t&&(e.flags|=4),16384&e.flags&&(t=22!==e.tag?xe():536870912,e.lanes|=t,ms|=t)}function tl(e,t){if(!ia)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function nl(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=65011712&a.subtreeFlags,r|=65011712&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function rl(e,t,n){var r=t.pendingProps;switch(na(t),t.tag){case 31:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:case 1:return nl(t),null;case 3:return n=t.stateNode,r=null,null!==e&&(r=e.memoizedState.cache),t.memoizedState.cache!==r&&(t.flags|=2048),wa(Aa),Q(),n.pendingContext&&(n.context=n.pendingContext,n.pendingContext=null),null!==e&&null!==e.child||(da(t)?Zu(t):null===e||e.memoizedState.isDehydrated&&!(256&t.flags)||(t.flags|=1024,ha())),nl(t),null;case 26:return n=t.memoizedState,null===e?(Zu(t),null!==n?(nl(t),Ju(t,n)):(nl(t),t.flags&=-16777217)):n?n!==e.memoizedState?(Zu(t),nl(t),Ju(t,n)):(nl(t),t.flags&=-16777217):(e.memoizedProps!==r&&Zu(t),nl(t),t.flags&=-16777217),null;case 27:G(t),n=W.current;var a=t.type;if(null!==e&&null!=t.stateNode)e.memoizedProps!==r&&Zu(t);else{if(!r){if(null===t.stateNode)throw Error(o(166));return nl(t),null}e=B.current,da(t)?ca(t):(e=_f(a,r,n),t.stateNode=e,Zu(t))}return nl(t),null;case 5:if(G(t),n=t.type,null!==e&&null!=t.stateNode)e.memoizedProps!==r&&Zu(t);else{if(!r){if(null===t.stateNode)throw Error(o(166));return nl(t),null}if(e=B.current,da(t))ca(t);else{switch(a=rf(W.current),e){case 1:e=a.createElementNS(\"http://www.w3.org/2000/svg\",n);break;case 2:e=a.createElementNS(\"http://www.w3.org/1998/Math/MathML\",n);break;default:switch(n){case\"svg\":e=a.createElementNS(\"http://www.w3.org/2000/svg\",n);break;case\"math\":e=a.createElementNS(\"http://www.w3.org/1998/Math/MathML\",n);break;case\"script\":(e=a.createElement(\"div\")).innerHTML=\"<script><\\/script>\",e=e.removeChild(e.firstChild);break;case\"select\":e=\"string\"==typeof r.is?a.createElement(\"select\",{is:r.is}):a.createElement(\"select\"),r.multiple?e.multiple=!0:r.size&&(e.size=r.size);break;default:e=\"string\"==typeof r.is?a.createElement(n,{is:r.is}):a.createElement(n)}}e[Ae]=t,e[Oe]=r;e:for(a=t.child;null!==a;){if(5===a.tag||6===a.tag)e.appendChild(a.stateNode);else if(4!==a.tag&&27!==a.tag&&null!==a.child){a.child.return=a,a=a.child;continue}if(a===t)break e;for(;null===a.sibling;){if(null===a.return||a.return===t)break e;a=a.return}a.sibling.return=a.return,a=a.sibling}t.stateNode=e;e:switch(ef(e,n,r),n){case\"button\":case\"input\":case\"select\":case\"textarea\":e=!!r.autoFocus;break e;case\"img\":e=!0;break e;default:e=!1}e&&Zu(t)}}return nl(t),t.flags&=-16777217,null;case 6:if(e&&null!=t.stateNode)e.memoizedProps!==r&&Zu(t);else{if(\"string\"!=typeof r&&null===t.stateNode)throw Error(o(166));if(e=W.current,da(t)){if(e=t.stateNode,n=t.memoizedProps,r=null,null!==(a=ra))switch(a.tag){case 27:case 5:r=a.memoizedProps}e[Ae]=t,(e=!!(e.nodeValue===n||null!==r&&!0===r.suppressHydrationWarning||Kc(e.nodeValue,n)))||sa(t)}else(e=rf(e).createTextNode(r))[Ae]=t,t.stateNode=e}return nl(t),null;case 13:if(r=t.memoizedState,null===e||null!==e.memoizedState&&null!==e.memoizedState.dehydrated){if(a=da(t),null!==r&&null!==r.dehydrated){if(null===e){if(!a)throw Error(o(318));if(!(a=null!==(a=t.memoizedState)?a.dehydrated:null))throw Error(o(317));a[Ae]=t}else pa(),!(128&t.flags)&&(t.memoizedState=null),t.flags|=4;nl(t),a=!1}else a=ha(),null!==e&&null!==e.memoizedState&&(e.memoizedState.hydrationErrors=a),a=!0;if(!a)return 256&t.flags?(su(t),t):(su(t),null)}if(su(t),128&t.flags)return t.lanes=n,t;if(n=null!==r,e=null!==e&&null!==e.memoizedState,n){a=null,null!==(r=t.child).alternate&&null!==r.alternate.memoizedState&&null!==r.alternate.memoizedState.cachePool&&(a=r.alternate.memoizedState.cachePool.pool);var i=null;null!==r.memoizedState&&null!==r.memoizedState.cachePool&&(i=r.memoizedState.cachePool.pool),i!==a&&(r.flags|=2048)}return n!==e&&n&&(t.child.flags|=8192),el(t,t.updateQueue),nl(t),null;case 4:return Q(),null===e&&Ic(t.stateNode.containerInfo),nl(t),null;case 10:return wa(t.type),nl(t),null;case 19:if(I(cu),null===(a=t.memoizedState))return nl(t),null;if(r=!!(128&t.flags),null===(i=a.rendering))if(r)tl(a,!1);else{if(0!==ds||null!==e&&128&e.flags)for(e=t.child;null!==e;){if(null!==(i=fu(e))){for(t.flags|=128,tl(a,!1),e=i.updateQueue,t.updateQueue=e,el(t,e),t.subtreeFlags=0,e=n,n=t.child;null!==n;)Ur(n,e),n=n.sibling;return $(cu,1&cu.current|2),t.child}e=e.sibling}null!==a.tail&&te()>ks&&(t.flags|=128,r=!0,tl(a,!1),t.lanes=4194304)}else{if(!r)if(null!==(e=fu(i))){if(t.flags|=128,r=!0,e=e.updateQueue,t.updateQueue=e,el(t,e),tl(a,!0),null===a.tail&&\"hidden\"===a.tailMode&&!i.alternate&&!ia)return nl(t),null}else 2*te()-a.renderingStartTime>ks&&536870912!==n&&(t.flags|=128,r=!0,tl(a,!1),t.lanes=4194304);a.isBackwards?(i.sibling=t.child,t.child=i):(null!==(e=a.last)?e.sibling=i:t.child=i,a.last=i)}return null!==a.tail?(t=a.tail,a.rendering=t,a.tail=t.sibling,a.renderingStartTime=te(),t.sibling=null,e=cu.current,$(cu,r?1&e|2:1&e),t):(nl(t),null);case 22:case 23:return su(t),mi(),r=null!==t.memoizedState,null!==e?null!==e.memoizedState!==r&&(t.flags|=8192):r&&(t.flags|=8192),r?!!(536870912&n)&&!(128&t.flags)&&(nl(t),6&t.subtreeFlags&&(t.flags|=8192)):nl(t),null!==(n=t.updateQueue)&&el(t,n.retryQueue),n=null,null!==e&&null!==e.memoizedState&&null!==e.memoizedState.cachePool&&(n=e.memoizedState.cachePool.pool),r=null,null!==t.memoizedState&&null!==t.memoizedState.cachePool&&(r=t.memoizedState.cachePool.pool),r!==n&&(t.flags|=2048),null!==e&&I($a),null;case 24:return n=null,null!==e&&(n=e.memoizedState.cache),t.memoizedState.cache!==n&&(t.flags|=2048),wa(Aa),nl(t),null;case 25:case 30:return null}throw Error(o(156,t.tag))}function al(e,t){switch(na(t),t.tag){case 1:return 65536&(e=t.flags)?(t.flags=-65537&e|128,t):null;case 3:return wa(Aa),Q(),65536&(e=t.flags)&&!(128&e)?(t.flags=-65537&e|128,t):null;case 26:case 27:case 5:return G(t),null;case 13:if(su(t),null!==(e=t.memoizedState)&&null!==e.dehydrated){if(null===t.alternate)throw Error(o(340));pa()}return 65536&(e=t.flags)?(t.flags=-65537&e|128,t):null;case 19:return I(cu),null;case 4:return Q(),null;case 10:return wa(t.type),null;case 22:case 23:return su(t),mi(),null!==e&&I($a),65536&(e=t.flags)?(t.flags=-65537&e|128,t):null;case 24:return wa(Aa),null;default:return null}}function il(e,t){switch(na(t),t.tag){case 3:wa(Aa),Q();break;case 26:case 27:case 5:G(t);break;case 4:Q();break;case 13:su(t);break;case 19:I(cu);break;case 10:wa(t.type);break;case 22:case 23:su(t),mi(),null!==e&&I($a);break;case 24:wa(Aa)}}function ol(e,t){try{var n=t.updateQueue,r=null!==n?n.lastEffect:null;if(null!==r){var a=r.next;n=a;do{if((n.tag&e)===e){r=void 0;var i=n.create,o=n.inst;r=i(),o.destroy=r}n=n.next}while(n!==a)}}catch(e){cc(t,t.return,e)}}function ul(e,t,n){try{var r=t.updateQueue,a=null!==r?r.lastEffect:null;if(null!==a){var i=a.next;r=i;do{if((r.tag&e)===e){var o=r.inst,u=o.destroy;if(void 0!==u){o.destroy=void 0,a=t;var 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shap summary plot (top 10)\n",
    "# ==============================================================================\n",
    "shap.initjs()\n",
    "shap.summary_plot(shap_values, X_train_sample, max_display=10, show=False)\n",
    "fig, ax = plt.gcf(), plt.gca()\n",
    "ax.set_title(\"SHAP Summary plot\")\n",
    "ax.tick_params(labelsize=8)\n",
    "fig.set_size_inches(6, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, X_train_sample, plot_type=\"bar\", plot_size=(6, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP Dependence Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SHAP dependence plots are visualizations used to understand the relationship between a feature and the model output by displaying how the value of a single feature affects predictions made by the model while considering interactions with other features. These plots are particularly useful for examining how a certain feature impacts the model's predictions across its range of values while considering interactions with other variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dependence plot for Temperature\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "shap.dependence_plot(\"Temperature\", shap_values, X_train_sample, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SHAP Explanations for Individual Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SHAP values not only allow for interpreting the general behavior of the model (Global Interpretability) but also serve as a powerful tool for analyzing individual predictions (Local Interpretability). This is especially useful when trying to understand why a model made a specific prediction for a given instance.\n",
    "\n",
    "To carry out this analysis, it is necessary to access the predictor values — lags and exogenous variables — at the time of the prediction. This can be achieved by using the `create_predict_X` method or by enabling the `return_predictors = True` argument in the `backtesting_forecaster` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP values of predict output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Suppose the forecaster is employed to predict the next 10 values of the series, and a specific prediction corresponding to the date '2014-12-28' requires explanation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Forecasting next 10 days\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "\n",
    "predictions = forecaster.predict(steps=10, exog=data_test['Temperature'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 2.5))\n",
    "data_test['Demand'].plot(ax=ax, label='Test')\n",
    "predictions.plot(ax=ax, label='Predictions', linestyle='--')\n",
    "ax.set_xlabel(None)\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `create_predict_X` is used to create the input matrix used internally by the forecaster's `predict` method. This matrix is then used to generate SHAP values for the forecasted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</th>\n",
       "      <th>lag_7</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-22</th>\n",
       "      <td>216483.631690</td>\n",
       "      <td>186486.896670</td>\n",
       "      <td>197129.766534</td>\n",
       "      <td>214934.022460</td>\n",
       "      <td>215507.677076</td>\n",
       "      <td>226093.767670</td>\n",
       "      <td>231923.044018</td>\n",
       "      <td>22.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-23</th>\n",
       "      <td>241514.532543</td>\n",
       "      <td>216483.631690</td>\n",
       "      <td>186486.896670</td>\n",
       "      <td>197129.766534</td>\n",
       "      <td>214934.022460</td>\n",
       "      <td>215507.677076</td>\n",
       "      <td>226093.767670</td>\n",
       "      <td>18.829167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-24</th>\n",
       "      <td>226165.936559</td>\n",
       "      <td>241514.532543</td>\n",
       "      <td>216483.631690</td>\n",
       "      <td>186486.896670</td>\n",
       "      <td>197129.766534</td>\n",
       "      <td>214934.022460</td>\n",
       "      <td>215507.677076</td>\n",
       "      <td>18.312500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    lag_1          lag_2          lag_3          lag_4  \\\n",
       "2014-12-22  216483.631690  186486.896670  197129.766534  214934.022460   \n",
       "2014-12-23  241514.532543  216483.631690  186486.896670  197129.766534   \n",
       "2014-12-24  226165.936559  241514.532543  216483.631690  186486.896670   \n",
       "\n",
       "                    lag_5          lag_6          lag_7  Temperature  \n",
       "2014-12-22  215507.677076  226093.767670  231923.044018    22.950000  \n",
       "2014-12-23  214934.022460  215507.677076  226093.767670    18.829167  \n",
       "2014-12-24  197129.766534  214934.022460  215507.677076    18.312500  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create input matrix used to forecast the next 10 steps\n",
    "# ==============================================================================\n",
    "X_predict = forecaster.create_predict_X(steps=10, exog=data_test['Temperature'])\n",
    "X_predict.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "If transformations or differentiation are included in the Forecaster, the output matrix will be in the transformed scale. To obtain the SHAP values in the original scale, it is necessary to reverse the transformations or differentiation. For more information, visit: [Extract training and prediction matrices](../user_guides/training-and-prediction-matrices.html).\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SHAP values for the predictions\n",
    "# ==============================================================================\n",
    "shap_values = explainer.shap_values(X_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x350 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Waterfall plot for a single prediction\n",
    "# ==============================================================================\n",
    "predicted_date = '2014-12-28'\n",
    "iloc_predicted_date = X_predict.index.get_loc(predicted_date)\n",
    "\n",
    "shap_values_single = explainer(X_predict)\n",
    "shap.plots.waterfall(shap_values_single[iloc_predicted_date], show=False)\n",
    "\n",
    "fig, ax = plt.gcf(), plt.gca()\n",
    "fig.set_size_inches(6, 3.5)\n",
    "ax_list = fig.axes\n",
    "ax = ax_list[0]\n",
    "ax.tick_params(labelsize=10)\n",
    "ax.set_title(\"Waterfall plot for a single prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The waterfall plot illustrates how different features pushed the model’s output higher (shown in red) or lower (shown in blue), relative to the average model prediction.\n",
    "\n",
    "+ `lag_1` had the largest negative impact, reducing the prediction by over 16000 units.\n",
    "\n",
    "+ `Temperature` contributed positively, increasing the prediction by around 7,214 units.\n",
    "\n",
    "The model prediction (f(x)) was 222766.341, while the expected value (E[f(x)]) across all predictions is 224,215.914. This means the specific inputs for this prediction led the model to forecast a value lower than average, largely due to the strong negative impact of `lag_1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same insights can be obtained using the `shap.force_plot method`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='i5D7FWDT9BT0WW37REF4H'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 224215.91441015498, \"outValue\": 222766.34065901474, \"link\": \"identity\", \"featureNames\": [\"lag_1\", \"lag_2\", \"lag_3\", \"lag_4\", \"lag_5\", \"lag_6\", \"lag_7\", \"Temperature\"], \"features\": {\"0\": {\"effect\": -16281.316883158266, \"value\": 195623.5918101613}, \"1\": {\"effect\": 5870.034024785922, \"value\": 184885.1458315842}, \"2\": {\"effect\": 1170.3339958046602, \"value\": 209260.94899085502}, \"3\": {\"effect\": -1549.5834499383684, \"value\": 220506.46870022157}, \"4\": {\"effect\": -906.871675367938, \"value\": 226165.93655852877}, \"5\": {\"effect\": 1445.0206882416435, \"value\": 241514.53254322908}, \"6\": {\"effect\": 1588.2271039584707, \"value\": 216483.63169}, \"7\": {\"effect\": 7214.582444533645, \"value\": 24.539583333333336}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('i5D7FWDT9BT0WW37REF4H')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x22c891a4ce0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Forceplot for a single prediction \n",
    "# ==============================================================================\n",
    "shap.force_plot(\n",
    "    base_value  = explainer.expected_value,\n",
    "    shap_values = shap_values_single.values[iloc_predicted_date],\n",
    "    features    = X_predict.iloc[iloc_predicted_date, :]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iG1AJ91995URKIPMIWWU4'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceArrayVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 224215.91441015498, \"link\": \"identity\", \"featureNames\": [\"lag_1\", \"lag_2\", \"lag_3\", \"lag_4\", \"lag_5\", \"lag_6\", \"lag_7\", \"Temperature\"], \"explanations\": [{\"outValue\": 241514.5325432292, \"simIndex\": 10.0, \"features\": {\"0\": {\"effect\": -5017.536675559431, \"value\": 216483.63169}, \"1\": {\"effect\": 6421.490192851052, \"value\": 186486.89667000002}, \"2\": {\"effect\": -579.0025762741299, \"value\": 197129.766534}, \"3\": {\"effect\": 329.2775761173882, \"value\": 214934.02246}, \"4\": {\"effect\": 465.95746888135477, \"value\": 215507.677076}, \"5\": {\"effect\": 2100.2233783696347, \"value\": 226093.76767}, \"6\": {\"effect\": 4371.6130514140505, \"value\": 231923.04401800002}, \"7\": {\"effect\": 9206.595717274316, \"value\": 22.95}}}, {\"outValue\": 226165.9365585289, \"simIndex\": 1.0, \"features\": {\"0\": {\"effect\": 10227.22613638466, 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       "    document.getElementById('iG1AJ91995URKIPMIWWU4')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x22c8944fc80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Force plot for the 10 predictions\n",
    "# ==============================================================================\n",
    "shap.force_plot(\n",
    "    base_value  = explainer.expected_value,\n",
    "    shap_values = shap_values,\n",
    "    features    = X_predict\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP values of backtesting_forecaster() output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analysis of individual predictions using SHAP values can be also apllied to predictions made in a backtesting process. For that, the `return_predictors=True` argument must be set in the `backtesting_forecaster` method. This will return a DataFrame with the predicted value ('pred'), the partition it belongs to ('fold'), and the value of the lags and exogenous variables used to make each prediction.\n",
    "\n",
    "In this scenario, a backtesting process is employed to train the model using data up to '2014-12-01 23:59:00'. The model then generates predictions in folds of 24 steps. SHAP values are subsequently computed for the forecast corresponding to the date '2014-12-16'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc650195d5004d3fac49b0f6da3a3a7f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</th>\n",
       "      <th>lag_7</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-02</th>\n",
       "      <td>0</td>\n",
       "      <td>231237.145266</td>\n",
       "      <td>237812.592388</td>\n",
       "      <td>234970.336660</td>\n",
       "      <td>189653.758108</td>\n",
       "      <td>202017.012448</td>\n",
       "      <td>214602.854760</td>\n",
       "      <td>218321.456402</td>\n",
       "      <td>214318.765210</td>\n",
       "      <td>19.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-03</th>\n",
       "      <td>0</td>\n",
       "      <td>227614.717135</td>\n",
       "      <td>231237.145266</td>\n",
       "      <td>237812.592388</td>\n",
       "      <td>234970.336660</td>\n",
       "      <td>189653.758108</td>\n",
       "      <td>202017.012448</td>\n",
       "      <td>214602.854760</td>\n",
       "      <td>218321.456402</td>\n",
       "      <td>19.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-04</th>\n",
       "      <td>0</td>\n",
       "      <td>229619.129116</td>\n",
       "      <td>227614.717135</td>\n",
       "      <td>231237.145266</td>\n",
       "      <td>237812.592388</td>\n",
       "      <td>234970.336660</td>\n",
       "      <td>189653.758108</td>\n",
       "      <td>202017.012448</td>\n",
       "      <td>214602.854760</td>\n",
       "      <td>21.702083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold           pred          lag_1          lag_2          lag_3  \\\n",
       "2014-12-02     0  231237.145266  237812.592388  234970.336660  189653.758108   \n",
       "2014-12-03     0  227614.717135  231237.145266  237812.592388  234970.336660   \n",
       "2014-12-04     0  229619.129116  227614.717135  231237.145266  237812.592388   \n",
       "\n",
       "                    lag_4          lag_5          lag_6          lag_7  \\\n",
       "2014-12-02  202017.012448  214602.854760  218321.456402  214318.765210   \n",
       "2014-12-03  189653.758108  202017.012448  214602.854760  218321.456402   \n",
       "2014-12-04  234970.336660  189653.758108  202017.012448  214602.854760   \n",
       "\n",
       "            Temperature  \n",
       "2014-12-02    19.833333  \n",
       "2014-12-03    19.616667  \n",
       "2014-12-04    21.702083  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting returning the predictors\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(steps= 24, initial_train_size = len(data.loc[:'2014-12-01 23:59:00']))\n",
    "_, predictions = backtesting_forecaster(\n",
    "                     forecaster        = forecaster,\n",
    "                     y                 = data['Demand'],\n",
    "                     exog              = data['Temperature'],\n",
    "                     cv                = cv,\n",
    "                     metric            = 'mean_absolute_error',\n",
    "                     return_predictors = True,\n",
    "                 )\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NeO6hv9EcM4F2To9Fw/li03+HM2S9DcdbdlBvApV0UDKFzv3pu8IpWEx/rOXUKynnYNpXhuDT8Leivx95tX/nDM+jx345+0+T9wglcs5euil6CCk4tjWcAWUF5vjZy+KbNgWg2/YcE12a9K2YMjEG9C2/dbN64ps/vbkNweqx0/oufAMKCCm7QVlID3dXk4AtJ/V4VMtD66APf6rBMicxOVnU4aT39FlwhixWdlEt3uuDuosuYzox0sneGN2XX8Xv6T8UDQENfahQnVxWqKuMTmT0eOMPMgMKoCkj+KL15BV1cRq2lx59ITh14ZrI4lA7qNDfGNWq5hQdJ1Rr1r1Dc1GDTKUh9GXKgI7j7KBuXMq4HNq4UGR86G9x5tINk67m7Lh8w7SGkNDfLzQ8UrwfjP9eTk4OuHz+rtkvPfR3pK7t7GhYpxqSkpPRu3NLgP6lo1AoRFcu1fZRt212UN1zevQBTtlv+nJK5Tj0M6H3OGWQ+3ZrI+6nzCAFTsY1l8Z/L+qOpfdQ+gA3Kz07tUTb5vXEl6nXxn+Z4djJ7n4iVBttbuoxCk5J5fKlRTc81fZRwERBX6nivth96LT4u1y+fi/bNdnZddlMyQHVLhLj46ZWyvsr/Zc7wxcBCkrp/ZUT9OXIXH2yoTTJ3Dnjxp0HZpMNOT0WDeeLU+eyPt5epF6tyuKYo0wtBYT5oVa1iuJLKpWYmXu/6M/T+tdj7MHjZ2bLX54Fh6aWjBkHtLaAA1BWYAxBpOGbv6H+01AfajgZUSG+IXCoXrmsONkZjxQ0dJnQt1Dq6qVbQ/bAUJuVXR7uLindcZ4mA2SyI+S56SCJ7KJv71SHSCfGo6cuiddJJzMKuA01Q1S/k18oy2wuaBP1iFl0SxJ3VxdxG/zc/Kh0Q9BMdV756YXtCM28HTn9u1Hg888f36JB7aq4efehGCwUFhGdmsWk4ya7fy8a+EP7eeSQHnjz1V7iOKaMBX2wzJi79IWDYgwyG+xDH1yGLkDj/USBqTnpB/pkhT7M6YvSi94n1PWc3QA0s+3TYDJCA8sMqH6PagZpYAUNPKJjjYIQQl8OjP8G9L4mdG7ICar7VioUuHj1dq6CT+Ll6SZuKVOWFRdnfUaK/v6Dx07DB2+/Kko5qO7SEBj9s20/vvtlucUCB3PHjVqjz2aaHjfOLzhuInIcgNJxby6Dbe5vbRBi5lyVm2Pxhe+DbH7ZN5xPjAe6Wpq7qwsio2LNfvnI6jydWaZcndJDJzX6+9oKDkBZgaETOn1LowxSiaJFRFc7ZdFonlADw7c+Gqz0NDBUBIfG3e/UBUIf3BQI9Bn5SYasDtXP5ER0jL67hOptqNYpJ3Siky5n6CRPJ03qiqPtpc9yGrIn+cnXxyvDhyu1i7KuNDAhK4Y58fx8vMwup8EtJK/dUC/ywnb4Zt6OnP7daEATBZ/rtuzLMFCDXm9Ov7j8u/2g+EcfZo3qVhNZVep+XL3wK7Tp/26Os6FZMfw9fb09zS739fHM/rpi4yCRSlCzbc5eb1Yy275v6nGk/ztTTwEFn0dOXcTw8V+alL1QbwkNyDJGM2rkJDNtQLWGHVs1FDXhtF4qy0nfzfoiMSnnlFHvz8Cew2ey9RwKHqhekf6VK11c1L6/NqgbRr/aG+5urpj4v+zX6lr6uKHSKHPnkJyi8wudz9MHoen/1sYy2/U5PRYN54tM3wcpbXjhelKCPONsu6VFx6rE4DX64pu+1CG752l7YXshMSvUDNnO1s3qikJ+6oI0PsFTppOCs5aN6xp1v6cFoDTSkLpAqN4zffBJJ4UyJYtl2Kbhw8q4y9SAMgu37j0SNTLURZHf6ORB26GuqPTBJ3VtG7qK8lMzM3Ox0shKOuEZfxkw5/7DALHPqGzBXHbR8DczzFjwov2fW4Z20jGUHp2km9avmaEduVW+tL7LlAappWduXtvsog8z6oL9+Ov5om6Njo1mDWpZfPBffHyiGGVPU/2Y+7tn1/mrt0Xmid4rlmJu/9EUXzWrlhe9AobBiOVLFxe3ew6dyVBzXb9WFVFmYIyeS19S6QtCrarZLwehjOrYj78XtcU0KwFdhcs4M5idY/r81VupA2hygzK8VNc68M3PRK01fQEy0KZsV2bB91JW7y9zx0fJ4r4igZBTlLGkL1zpGbq+X3TuycuxePWW/jzQrFHmx1t20IA26mWgv212JpDXGrLLOfh7Xb/lLz7jaLaU9Oj8QOdpS5zXbAEHoKxA0XRLhEZy0gnk+NmMNXzUDU/zOtIgEmKcATUMHqETo/HJn4K3WZ+/l6GWkhi6A+nEac7vKzaLgTNzvnrfbFBF01tYKjCkuf0ogKtTo6JJUTidVGZ8MhY+L+gCt4RJbw0Vr8mAXvtn740UP1OWLyvULUR1jHTS/mTccJNl9OVg9LBe4kOcMnzZ3f+5QaPrqSuKMt6UnTRGxxa1hbJlue1GNWY45tIHu9QFSTMC5ERmdWZUAkIsXVdGfy8qGfBwc8WksUMyjPQelIPBYn+s3Cxuf/p8QuqFFozRB3L6v8WLDOzVPkOASBllai9N1WOo/zNMcZT+b0A9It999o7ZdRvmlvxh+nhxvBqj7KYhW58eZZ3GTZkljmGqy/zth08zDApMPabNDHKjGk7q6Rk1pCc6tGqYaVe/IYChkiFz3dlUk0llBQmJaccE9RhRBtHcdi2JBiZRzSYN5EofbE6dODLXgyTpucYDy+jL+Ptj9MclTWeUX8fi7oOnxd+sf7e2Iptu7njLDuqdoNptyqx//uFocRwZo3O68bGW1XGSGcN++GziSJMgl36e+r7+PJ3+aoH2irvgWYE6dvaKOIFSBi2zQSQUlNKJnzI2+umbwk1qxjbvOixGyO5d97PIhFLNDI1cTUxKEsX86Qu0KWtHdaR9u7aBOlkjJt+mrOu/Ow6Kgnp6w4v5I4f2xIltv4t10v3UDUIfDE0b1BKB2ZRvF+b59dN2aTqn994cjAP/zsfuQ6dEwXzLRrXF9ijYpkuT5qd7/k9wcMMCk3lAaWAEXSDAOHDMDE3cTBlG6h6k+USpbIKm1urduZUYnDPt+8Umo8xftP9zg4L4j778BYtnfYoNf80UE8JTsEmXe6UR6ZRFt9QlUKkb1f/xMzHfJA1soWOMPlA6tWkspkPJyXWe/5o7Vcwbe+HKbf20LBJ9poyyeDQ5Oc1vaWk0dQ5lpmm+x/q1q4qpa4oW8Rbzth44dk6UAND0NNnpvfh23jJ8NvF1HN+6WMyHShdrcHZ2EoNnKJspBjWNz/4UYgePnRdTj23dmzYvI+0PmnXAeMoomqaN1k1zOW79+0fxcxEfTzH/Jx1f5mryaEoaOk5p8npqLwWGNGiwqK+PmOqNZg3IbCojOkfR5V/pnEKDtP6aPRVjJ89MDYipV4ZqPP+a8xn2HzuPhIREUTJEU5hRADvmo5mipIKmcjp76YYY5EJfLiiYo/cMdbPX7ThC3EfZtz9nfyZe490H1AMUJgJrel9SPSpd6MD4uL9w9Y74gk7ZWZofk7KxdIxSxtdS6CILsxatEgHj3vW/YOvuo6Lbm86zNME5zTtK74WcoHpcCqgPbFgganhphgb6e1Iwt2zdjgxTMFnyWKT99smM+aKOeNMSfYbbcLxVrVRWDH6lqfOyY9rM31C1YllRx01fKGkcAgXrpUsWRbvmDTBq0ozUifJpBgWa+H/W5xNEDwoNEqKej6XrdmQZ/NPfnsZC0HmavmwbxgfQF2v6YrZpp+kUTPaKA1BWoOgb5M27j8RJl+aYo+7v9IxH/xlnPw1obsBHAcHiA4CCxvCIKHECnrVwlZiYPD36MKH59ehkSleuMIxgptHHhgCI5nOj7tARg7uLq2VQJpQKwSmooav7bNxxyGL74MeFK8UHIU2cT3M30omdrqDxw4IVYu66/Pb2Jz+YXAmJPhh+WrQK85f8m63nUxam98iP8d7oQejesQXeGtFPZGkuXacrIW3KMLl5dvZ/blBA0XfUp5j45mBx4ndzoytiRYr5+WhqF0tdBpWChCFjp4nsA2Xg6EOLAiTaxuIVm8Wo7Oyi4J0CZLoSS8dWjZCQlISAwBB8M28p/l6/K9fT22SFJsXuM+pjTKErIbVsJIJdClxoblP6YKYANLs1ZTTPJk3rMnpYbzSpV118UFLNY1BoGFZu2J1hbsIX+X3VFuw6eErMQFGhSyvExSWIL3t0JSTjGSboGKKayk8mjBA1mvTlh45bmqT75z/W49BG8182KIikAOG1Ad3EFyQKgKj05fTF66I7Pyv0oU/lEYmJSWJ7y36ejtEffCsGPNJ2S5XwFV+qxo0cIHpeqOfGMIcuBYM01+vbI/qJLypD+3SCVqdFMI3Kv+Uv5r80TLtFXzzmL/1XBE3tWzQQmU967Vdu3BdZXJpX2BjVg341eYw4jqgHgN5LFIBbMgAldD4IDA7D2yP6inIElSpe7Mtv5i0Tk+zntA6RvuwOfft/4opc9J6hkhO6EhIF2OmvhJQfxyJ94aaAlK6E1KdzKzG7yenz19Dn9Y8xfvQgNEetbNfr9hn5Mca+1ld8BtGxpdFqxGwC9KXmzv20K9nRuZDqeulLzJjhfUVvE305zyoAJe9OmSUGJlJtOM38QO4+eIKpMxeJ80RhIYFfw5yPpGCM2Z3MLvvGXl50pS/qAqUJ+3N6VST2cqIvkJf3rxBZXQrEcnIpTsMlKBkjXAPKGGOFnLk6OboCD80oQZk4c3PDspcbldWkr/WkAVlffPSmqEf87+BJq7WNFQ7cBc8YY4XcrlVzxOjqW/cfIz4+QdT8UgmAVCrBp98szNHFG9jLoWfHFuJSs1TH+CyIauLdxMjsiuVKiTroJWu2W7uJzM5xAMoYY4Xcyg3/iUEMVDPo6uwkBkIcOnkRvy3fmDpggjFjF6/eEQN5aFAYzVhCnqTUPlPdprlLhzKWE1wDyhhjjDHGChTXgDLGGGOMsQLFAShjjDHGGCtQHIAyxhhjjLECxQEoY4wxxhgrUByAMsYYY4yxAsUBKCsQEolEXHGFrrX+6OwmcTtu1AAc2bRILMupEYO64eyuJVAqeCYxxhhjzN7wpzcrEKOG9BCTGtO1s2/ceYiYWBXmfPU+ZsxdIq65nFPrt+7HR+8Mw4hB3XN1HeGcKlvSF83rVUHVCsXh4+kGVVwi/J8GY8u+cwgJi8rx4wzkMin6dGyEpnUrw9nJAQFB4diy/yxu3g8weVyVcsXx0ZvmL6H5/eLNePA0RPxcpkQR9OvUGBVKFwXF9f5PQrBh92k8DQrL8Dw/b3f06dQYlcoUhYuTI8KjYnHmyj3sOX4ZyclZX5O8uJ8XerdvKLbn4eqMpGQ1AkMjsOfYZVy5/ThX+yOn+44xxpj94nlAWYH4b/Vcccm/YeO+EL+PGd4Hk98ZhrodR+T6KizT3h+JPl1bF8j1hd96pRMqlSmG89f88TQ4TARd7ZrWhINSgR9+34xnIRE5epzBm4M7oGHNCth/8qoIsprXr4JyJf0we8k23H8cnCEApcc9Cgg1Wce1u09EsFa6uA8+GdsXEVGxOHL2JqQSCdo2rSGCy5mLNyH4eVoQ5+XugukTBiE+IQlHzt5AXHyiCFpbNKiKSzcfYtHqPVnuj1qVS6ND81rwfxKMyOg4kYluULM8KpcrjpVbjuDouVs53h853XeMMcbsFwegLN9RAHH3xD/46bfV+OXP9eK+vet+wc27DzHxf3Nyvd7a1Sti95p5GDx2Go6fvYL8RMHZo2eh0Gi0JhnEzycMwoUbD7Dk34M5ehwpV9IXn73TH//+dwp7j+vbT9de/mLCIMSo4vHjH1szBKCL1+7FhesPzLZxwmvdUKG0H6bPWwdVfKK4z93VCTMmDcWNewHiuQbd29RDv85N8OWv/yDQKLAbNaCdCII/+HYZ4hJydqUTKqWY9m5/KORyfPHL+hzvj5w8ljHGmH3jGlCWr2Z/8R4enNkoAqspE0bg2aVtuLj3b9SsWh5HT1/K8Phift7wP70Bc76caHJ/66Z1Re3ol5PTsp1Xb94XWdWu7Zvm++ugTJ9xYERCwqNFVq5YEc8cP440qFlBPPbouZup96nVGhy/cBsVyxQTWcrMAnq6hnd6lcoWw03/gNTgk0THxuPOw0DUrloGDsq0ihtHB2XK8jiTdUTFxEGr1UKd7jVkB5VSRESp4OSoX3dO90dOHssYY8y+cQ0oy1cbdx5GslqD1wd3x/QffkdEVAzKlS6Gye8OFwFkekEh4VizaQ+GD+yK2YvXICAwFJXKlcLiWVNw8Ph5fDX7L5PHX7t1H43rVs+yDRSsGQdFWaGu6JyUpFKGMTtdw+YeR13mwWFRSEg0LUF4mFLPScsjolUmy0b2byuCRwrU7j0Kwobdp/Do2XOxjIJ8c7WbVJ+pkMtQws87tVb0zoNn6NamHl7v1xbbDpyHKi4BFcsURdsmNXDg1HXxnOygrneFQgYnByXqViuHmpVL49y1+7naH5Z4LGOMMfvAASjLV9Q13qppXaji4rFk7XaRJaPBSORxQFqNo7Ffl/yDV/t3wfg3BmHWgpX4+5fpePosGO9OmZVhwNKjp8EY1LNGlm2gusLMBvCkN3X2aoRFxmbrsU3rVoKXhyu2Hjifq8d5uDkjOsY0A2nIQuqXp2VAKSN5/ro/rt15gti4BBT39USXlnUxeUwf/PjHFjwJDEPw80iUL+0nusIN+0kmk6J8KT/xs6dRRvX6vafYsu8surepj3rVy6Xev/PQBWzZfw7ZNahbMxG0EsqcXrzxEGu3H8/V/sjrYxljjNkPDkBZvqtRuRxu33+cGhR5ebojOVmNuPgEs4+nLOjqjbsxbEBX1K5WAY6ODhg45jPEJ6R1LRtERcfCyckBTo4OZpcTGgE+d+mObLU1KjY+W48rWsQDr/ZqhfuPg3Dy4p1cPY6yh8majBlLyhjrl8tMuqd/X5sWsF+59UjUgn4+fhD6d26CX5bvwuEzNzC8T2u83q+NGI1OgWiPdg3EYJ706yNhkTG4+yhQrEcVn4BaVcqgW5v6Yh8cOn09W/uBBkXR8z3dndGwVgWRbZbJZHnebzl9LGOMMfvCASjLdzWqlMehkxdy9JxFyzdh9Ku9UaNyefQb/akISs0xTCGa1VRONJjmlr/ptEZ5QV3C743oLkaQL167L9Ntv+hxomvcTLBG3eX65VlPhRQaHo1Ltx6ifo3yItikke+ULezSso4YzW7ozqdglALRBKPZBhrVrojX+rTB9J/XITKlm5+ylzRyfkCXJjh75Z5JLWlmaGS9YXT9qUt38f7IHhj/WlcxNVRu91tOH8sYY8z+cADK8pW7mwtKFvfFrbuPUu+LiIyGQiGHi7OT6Jo3hyatJzK5FJFRmXeJe7i7ikxqQmLmI7apG9rFySFb7Y1RJWQZ7Dg6KPDe691FTelPf25N7S7PzePoPuNu8dTX5KbPWEbFmNZ/mkODfihgpQFGVEtK3ep7j10W83TGJybhWXCEmBeUhBhNw9SuSQ08DnyeGnwaXL71SASvpYsXyVXQfuG6P17r20ZkL42nfcrufsvpYxljjNknDkBZvqpeWV9feONu2tRB9x4+FbdlShYVUzGl9+7I/hjWvwumzlyE6ZNGi2B08te/ml0/reOu/5Ms21CxdFGL1IDSIB+a6qiojwfmLduBwNDIPD2OSgOqli8hAi7jgUiGmk2q63yRIt5uIpNqPJcqZXyN5xCtVrGkmGQ+6HlaO9xcncSAK3PBurg1M8o+O2gKJkKDknK6P3L6WMYYY/aLA1CWr2pWKS9ub95JCzTPX9ZPUl63RqUMAWi39s0wdeJI/LhwFZat24nyZUpg5JAe+PnP9XjyLOOgpVrVKmLTzsNZtsESNaDUxT12SEcxV+XCVbvFFYby8jhCg4q6tKqL1o2qp80DKpOieYOqoubTeAS8q7OjGHxkrFQxb9StWlZMRJ9Z0rZRrQoioP1n10mTx1A2tHqlUvDz8TC5ylDj2hXFYKKnwfqSBxrh7u3hKrZNk90buLk4imyxMar/bFavMpKS9FdFyun+yMljGWOM2TcOQFm+ql6lHJ4FU1dvWlaRRr9T4Nm6WT2s3bLPZGL5+d99JKZuMkxYv3DZRnG5TXNZUHq8t6c7dh86lWUbLFEDOrhbMzFanLqoXZwdxOhsY6cv38vR48jDp6FiyiIaROTm4oTQ8Cg0q1cFRTzdsGKTaVA9dmhHMcXS/SfBiImNF13srRtVE9nPTXvPiMdULlsMPds3EJPO07RKNCK+Rf2quHbnMQ6cumayPqoLpSmTPh7TGwdPXxfBJc0VWrtKGTEvqaHbu3xJP5E9pqmath9MG4lOg50oy0mDmChQpoFOTepWQnFfLxHsJiapc7w/cvJYxhhj9o0DUJbvXfDmutkp8Pz43WFiTkuq3yzu54O/f56Oa7f88bFRoBkcGi4eO6x/5wxZ0N6dW+HpsxAcO5O/V0EipYr7iNu61cqKf+kZgqPsPs5g6YZDCO8YKzKHzo5KkXmcv/I/3H0UZPI4ujxm0zqV0alFbRH40ZWSaNAQBYU0GIlQIKjV6tClVR04KhV4Hhkjriu/78RVcb8xWj9N39SrQ0O0a1JT1MjS4zfvPYPdxy6/cH+cu+aPlg2qok3jGiI7S39Dmo90454zYoR+TvdbbvYdY4wx+8WX4mRW4ebqjJPb/8C385Zhzea0S0RmF01hdHrnX5i/9F/8tXpbvrSRMcYYY/mDL8XJrCImNg6Llm3EuyMHiNq/nBrat5O4bOWKf3blS/sYY4wxln84A8oYY4wxxgoUZ0AZY4wxxliB4gCUMcYYY4wVKA5AGWOMMcZYgeIAlDHGGGOMFSgOQBljjDHGWIHiAJQxxhhjjBUoDkCZzVBIpahWsri4tTaZRAJPJydxy/IH7+P8xfvXvvYxnfeKubvbxPnPVvAxXLj3MR/pzGYo5XIkJiSJW2uTSaXwcnYWtyx/8D7OX7x/7Wsf03nPSaGwifOfreBjuHDvY/6rMsYYY4yxAsUBKGOMMcYYK1AcgDLGGGOMsQLFAShjjDHGGCtQHIAyxhhjjLECxQEoY4wxxhgrUByAMsYYY4yxAsUBKGOMMcYYK1AcgDLGGGOMsQLFAShjjDHGGCtQfM0vZvsqtAC8ShfoJtUSKaKcnKCOjwd02gLd9suC93H+4v1rZ/s4NhB4fh0o3wxwLW6pJto1PoZtaB/f+A+Ij7LotjkAZbbNyRPoOhVwLVKgm6W3YXiBbvHlw/s4f/H+tbN9fO8gcOQ6UKM7UKm9pdZq1/gYtpF9rNPCSR2N+Mu7LLpt7oJntq3ZKMDZy9qtYIwxxl5OOsDdzcXiq+UAlNkuuQNQpR0glVm7JYwxxthLq23T2hZfJwegzHbVGwA4e1u7FYwxxhizMA5AmW2SSIF6AwG50totYYwxxl5qR89cs/g6eRASs02V23HtJ2P5YGRNCZZ1N1/WUmyhGsFxab8/GCtDOQ9Jhsf9dkmLd/eZjpj1cAB+bCNF/8oSOCuAM4E6fHRIi4shGbfjqgCmN5dicFUJSrgAz+OBk4E6vL5Ti3h11u0v5gJ81UKKzuUkKOYMPFMBW+7p8O0pLcITTB9LLX+7rgRv15WiqhcQpwYuh+rwwUEtroRmvR3GWJoSxXwQCMviAJTZpuajAQfLFz0zxvSmH9PgQbpZVSITMz7uYrAOs8+ZBpt3InQZAr0dA2So6wvMOqsVAeW4elIcGipDwxUa3ItMe6y7Ejj8igylXIHfr+hwL1IHX2egdUkJHGTIMgB1UQAnh8nE7cJLOjyJ0aGurwQT6kvQvrR+W8YtW9JNiuHVJVh+Q4f5F3XiefX9JPBzphabvgbGWOYqlimO87AsDkCZ7aE5Pwt42iXGXja7HuhwPvjFjwuI1WHVzayDtUFVJWhZUoJBWzXYcEf/2PW3NbjzpgxftZRi+I60AHZmGynKugMNVmjw0CgA/jEbAWGfihKRke25UYOd/obH6xCeIMUXLaSo6wdcSsm4UnZ1VC0p+m/WYPM943Vz4MlYTmm1ln/fcA0osz3VOgPOntZuBWOFHnWFSzP2sGegkEJ0q2dmUBUJglQ6bEwJPgllQdff1qFvJQmUsrRu+jdqSkTmk4JPWq9hWXa4O+hvg1WmH4aBKb8bZ08/bCjF6UCdCD7pJWbVfsZY1v7deRSwZgZ0aI8WqFOtLIp4uWHGgg14GhSWuqxmpVLo26kxZDIpkpLVWLX1KJ4GZZzetERRLwzr1QpuLk7QaLV4+DQUa7YfQ7JaI5YvnvEWAoLDodXqvzGv3XEC9x4F5fmFzp/xHto0byh+dnd3Q3KyGvE08z+A96b/jMMnL8LWjXmlO8KiYrFpl+UPBJvi4mPtFjBW6B0cKoObUoJEtQ67H+rrNY27yg06lJEgbpIMcqkED6N0mHtei18umAaA1K19IViXIbdIdaBUf1nFC7j2HGhVUgInhQT3IrT4p48U/SpJRAB88hkwfp8Gl19Ql3nkqQ4arQ4/d5Dho0MaPI0B6vhKMK2pFJvuanE75SPHTQk0Ka7vpv+2lRTvNZCI1+ofqcOUo1r8c5uzoIzlRJ8uLbD19n5YLQC9cN0fu49dxsdj+pjc7+yoxOjBHfDTX9sQGBKBSmWLYfSgDvh6/r8Z1qFO1mDN9uMiyJRIJBgzuAO6tq6H7QfTqgtm/bkV8QlJsKQJ039N/fnwvz9j275T+Om3NbAlCoUMycn6QNycbu2bICQ8JlcB6IvWbVMcXK3dAsYKLRqIs/SaFgcf6xCdBDQsKsGHjSQ4MUwmusUpqDO4EqrDsQCdCOx8nIBRNaUi+CvhqsWUI2nd6sVdKDjMuK1Alf62hKsE157rUDllXOHM1lLcj4IYdERZUeo+PzBEhprLNAhKeY45N8OAt/Zo8VM7KU4NT/v4WnZNizG709pT0ZMyuxK8UhVQ64BPDmsRlQi831CKtb2kiE7UiqCbMZY9jkrLV2zmaI13M8lE+nq7QxWXIIJPQhlLbw9XlC7ugyeBaVlSEhIenfqzTqfDw4BQlPCz3mjnciX9sGDmhyhR1A9yuQyXb9zFGx98j2S1Glf2LcX5q7dRq2pFeHi4YevuIwgOCcXQvl3h5uaC5et34ttfVsDH0xWndvyJk+euoG6NylA6KLF20158NXeZ2EbnVvXxxeQxcHF2gkwmw6oNu/HDwtWoUq44Dm3+HfsOnUCdGpVx9MwVHD5xDlPeGwmFQiEeu23PUXw283cM79ceDerWEuu7sm8Zduw/gZNnL2PeNx+jQrNB4v7GtStjy4o5KFGvt9l1z160OtPXahMUTtZuAWN2hbqWs9uFnZjy/ZOyf8YZQBpBvvshcOQVmcgkGo9u77vZdPDR0msa7BooxYcNJfj1AtWH6u93kqet31iCOm25ocuf0NY7rtdAlaz//WKIRgSU4+tJMf141tf8pm1SZnXnAy0eRQOtS0kwsb4Ez+Ol+Piw1mQ7RZwlaLpSjTMpH11b72vEyP7/NZNi90M7+ULOmA14/Mzy00ZYJKQNDouCi7MjKpQuCv8nwaKb3slRKbrq0wegxpQKOVo1rIZNe8+Y3P/BqJ6QyqS4fT8AW/afE1366TWpUwldWtUxu95j527h0JkbL2w3ZWD/mPUJ1m47iL/W7BBZwv9W/IiP330V3/26QjzGy8MdTXu9jQY1K2DDkh+x78hpNOo+Bp1b1cMfcz/Hr0s3pq6Lgsa6nd9AvRoV8M8f32HPkXO4//Axfvz8PQyf8DVu3HmI0sWL4L/Vc7H36DnExsSk/mFfnzRT/Ozn44Gte98Vr5kC8z1rf8bGnUewavNB9O/eRmRAx302Rzy2T6emL3yNhnVT+/asmpXlazWmkEqhlBfsGDUpXfXo8bEC3SZj9qxNaYkYaZ4d1ZaoU7uo0zseAJwOBDqVfXFB6NzzOnQrL0W70pLUwUlUe0kj2NNzTDmFGGozDbfb7utSg09C26bu8RYls952ixLA9gFSNFulSR1ARQF0dKJEZFGXXNNnSQ3boXUagk9C26Rtv1ZDApkE0HASlLFsuXv/KVyUpvNyq5Ly1lNtkQgjITEZi9fuRf/OjeGgVMD/SQiehYSLWp3MUK3o2KEdcePeU1y6+TD1/ik/rUJElEoEp8P7tMbArk1Fl316Z67cE//yolgRD1SsUA6Txg7F+28OFvdREFmlQunUx6zdsl/Uo1678whxcXFYvVlfA3H+6j0kJSahdAk/BAXrvxn8smSDuL10wx/3/B+iZ8dmuHG7CHy8vbB24Zep65Qr5KhfszKOnrogfl+wbFPqshJ+3lj563QUL+YnMsSuri5oUr86zl6+lavXaFh3dl6rsUoliyEhzsycLPkowt2vQLfHmL27FabDqF3Zy+QFpmQrM0NTGlX1enEASo8j3o5G61bpu+HTM9z3LFb/nGcpbTCea9QgJA7wcsh6+1RPGqxChtH7W+/r8FVLCVqUkOBmmO6F21HKJGJKJipBYIy9mFSrg5+bm8l9D8IyTzBmh8VSXHceBGL2g+36lcqk+PHTEald8ulJpRKMHdIRUTFxWLfzhMkyCj4JZQAPn7mO1/q2MbsOS2RAIdHPBdd31BT4P35m9iFx8fogjIJB+hcXr5/pWKujE6pOdJNnhh4jgUQ8p06nURmWUzc5UaWsk8z9aiJu3n+C7iM+gUajxenti+DkaHSmN6JWqyExGsLq4pwyRNRI6rqz8VqN3QsIKvAMqOLW3gLdHmP2jgKsv69bJo1XwUOC0PjsPY4YP/ZSiE50haefXbNpcQlUyTrcSfkoOB+sX1rSTJl3CVfgVnjWr6WoMyAzM3cLjaYncmlaQBwYq8t0O/HJOsRw8MlYtlWvVg7nr+yCJVkswnB3dUJ0rP6M1LNdA9z2f4ZQo3pP0+CzE1TxiVi55WiGwUzJGo0YLEPxUqNaFfE48LnZ7VkiAxr8PBIPHj3BV5NHY9QH34mAr0KZYnBwcMDNu49yvL7xI/vj5LlrqF21HCpVKIev563Eg0dP8b8PtXhnRF/8tmKLeFzLRjVx/Y759Ts7OeLx02DRlv5dm6N4sWKpy6jO1t01Lc1wxz9AFNrXqFwWt+4/xqihPSz2WpO1WiTnMb2eUy4Rz4B037AYY5ZVxEk/RZKx7uUlaFRMgp/Pp9VfejlCDNwx7siiAG9KE6kYOU+DmAz+vaPD4KpSDKgiSZ0HlAYt0Vyc1OWdlJKkpUCUglWamomWh6W0o3NZCcq4S/DrRa3JhPXFXfWZW0Omkp7ftbwEbUtLcPhJ2vZfrSZNnTTfYN1tHSY1lIqygn2P0tpE2z7wJOOIfcZY5vyfBuW5yz1PASh1ideuUhrurs54f2R30fU+fd46saxPx0aoXLa4CDCpC3755sOpz+vdoaHIdh45e1MElQ1qlseToDD8b9wAsfz+42DRzV7M11Nsg04MMqkEj5+FZciQWnpi1VGTvsOCbz/A5b3LQKcktVqDqd8tznEAStlRrUaNS3uXwsFBiZX//oeT5/XXTh0/5Ud8/clbGD9qACRSKaKiYzFw7DSz65n7+1pM/3AMhg3ogoCgUDwLTCtgWvHvHsz5elLqICQanLRx215sWjJTBKenzl8pkNea75LTXU+PMWYxNNr9YogO54J0IsBsUFSC0bUkeBytw3entSaTvtNgHQouH0TpRJf7sOpS1PaV4LMjGpPubXrMyWc6LO0mRQ2ftCshUZ3lF+kGFdFlMPcOluLYKzIsvkKj4CViUNPtcB0WXUoLC+mSnnTJUCoxMGR551/U4o1aEmzrL8WvF3R4FK0TwSi1a89DrUm958zTWgypKsGGPlLMOU+vVYd36kpFtnTq0awHOjHGTPl4ucPMRBd5IoFfQ/4imEeGUfD9Rn+G67cfWLs5dosKnKnGJKThKKjq6utUGWOWNaOlFD0rSFDeQz85O3VX7/DX4asTWlEfadCgKPBFc6kIUH2dgCSt/ipDv1zQioAzPU8HYFZb/dyeTgrgbBAw+VDaYCFjHctIMKOVFPV89dNC0fZpqiTjoNZwzXrjAJTQnKLftJKK7n26LjzVe/5zRycC3fSX8aTX+FNbKTqWlYjAk4Jkmgf0XN6nlrY4l3sH4XfkJ4S0mQxVpfbWbg5jabRaDMFerJ/9BSyJL8XJbM/9E0CN3oDCfO0rYyz3aJqj6RnHdWZwITjjNExZoevIj92jxdg9L37s/sc67F+d9eApCjr/vp5xBhTqhh+yLXvtomvdD9zK2U7GbBEHoBYQFhmLyi1fsXYzCo+nF4G4CMBDP0iLMcYYY9bzzw7LX4GRrwXPbI9OC1zfBWhsZIJ8xhhj7CXWo30Ti6+TA1Bmm86uAlR5m2OMMcYYY3lnbprHvOIAlNmmJBXw+Jy1W8EYY4y99AKCM7mMWh5wAMps17HFQKz5eWAZY4wxVjCu3067YqWlcADKbFdMCBCat4sNMMYYYyxvurRpAEvjAJTZtiML9SPiGWOMMVZo8DRMzLaF3AEenASKVCrY7UoApUyGJI3G9OLWzHJ4H+cv3r/2tY+jA9Nug+9YonX2j49h29jHEgku3z5k8U1zAMps386vC3yT9IYs6emJgMhI/RuTWRzv4/zF+9fO9rFSCbi5AUd/Ayx8zW17xcew7ezjUi3r4baFt81d8IwxxhhjLFNtm9aBpXEAyhhjjDHGChQHoIwxxhhjLFPfzl8DS+MAlDHGGGOMZeqtYT1gaTwIibFCwFknwaAEJ9gTuVQKL8gREecEtVZr7eYUOrx/7Wsfh0AqBnm0TlLCL54/mgkfwwW/j3c5JCBUmnFf+3p7WH7bFl8jY6zAtUpywO8xPnCgOTXsSRT9Z/lrDLMUvH/tZh9vkSTjAyThnQQ39I1RWKJlhQMfwwW6jzcnxqG/Z8YrEN5/nDJNmAVxFzxjhYTGzmJPxhhjtqWxWgk/bcbQcMeB0xbfFgegjBUScrnM2k1gjDFmx4prZZgem7G7feKofhbfFgegjDHGGGMMUkjQJ8lJjCvI/20xxgoFtZqvFMIYYyxvimqlGB/nanLfdu6CZ4xlRirlIlBWCP3+LqDbCGyban65qyPwwwjAfxGQsA54+gfwz8eAk9L0cR7OwOJ3gJClQOxq4MBXQP0KGdfnoACmDACu/wyo1ujXt34yUKN09tpbzEu/HWpP3Brg3kJg9ijA2/QDXZBIgHe6AhdnA0sm6O+bOgioUy5722IsHzhAinfiXSE1uja8I10q1sJ4FDxjhYRUKgU0PFUJK0QaVgRGtQfiE80vd3cGDs8ASvkAv+8F7gUCvh5A6+r6QDI+KS3Q2/E/oG5ZYNYW4Hk0MK4bcOhroOHH+ucZrJoE9GkM/LEPuLANKOEFjO8OnJwJ1P4AeByaeXtdHPWPc3EAFv4HPAkD6pYDJnQH2tfSb0tn9Km+ZDwwvA2w/BCw5ZD+Pmqbn+WnvGEsJ3y1MgxNdMYaxzjxe6dW9XHkzFVYEgegjDHGCs7Br4GHIcAb81/82F/e1AdnHTO5DvXM14CyvkCDyfp1Gvy4yfRxg5oDLasBg2YBG07q71t/ArgzH/hqKDB8nv6+Et7AwObArM3AJ8vTnn/0pr7dA5oC87Zn3l4KXMv5AT2/BXaeT7s/PAb4Yqg+GL30QH/f4BbAqA5A/x+AzacB92SgJGV89wDRPA0Tsy43SDFV5Y41DnHIr9n9uAuesUIiOVlt7SYwZjkj2gG1ygDTVptfTl3qb7TXZz4p+FTIAWUmORUKQIMigI2n0u6jTCMFoX2bpD3PLeViDsFiYsQ0gRH6W0NGNTPuhudHvvj5H/YBTt/RB5+UoVVy0Mlsb0R8q2T9/KCzFv9j8fVzAMpYIaHgaZhYYWGo6/xuQ8ZgzqBVdcDJQd99TjWfVG8ZvxY49p0+02isfnnggr9p9zc5c1ffbV6lhP73+0HAk+fAR32AXo2Akj5A40rAb28D/kHA2mNZt/vIDUCjAX5+E2haRf/87g2AaYOATaeB2wFpgW6TSsDZe8C3w4GolcDSlBpQeh5jNsBHJ8NMlb4cZFi/DhZfv9wScw+OHdIRxX09kaTWICY2Hqu3HUNoeLRYPrJ/W1QsU1QsS0xMxvpdJ/EoQF9D07dTY9StVhbalEts/Xf0Ms5dvS9+fufVziji5Za6nZJFfbBozR5cufUI7ZvVROtG1cX9Op0Oe45dxunL98Tv9aqXQ+8ODcX9MpkMl24+xJZ9ZzNtf6/2DdGkTkWoNVrEqhIwZ6m+e+XD0b3g4+mK+AT9N9aTF+9i/8mrL3xNeTX3ywkoV6oo+o+ZDkvbvXo2ivv5pP7u4+ONOYuWY/bv/1p8W8wKKIuCdB+wjNmjz4fo6z7nbsv8MZWLp3XDU+D4+i/6rOgXQ/QDjGpO0mc9SXEvfXCYniEzSV3v1x4DNJPEwB+B1R+YDno6dw9oMRWI0tfDZermU+Ct34CfRgKnvk+7f9kBYMzCtN8rFqOibeCVVvptUnf/g7v6ZRN6ANITwO6LL9xNjOW3Smo5qqjlIgazNIvUgB49exPX7j4RP7drWhMj+rXBnCX6QO7izYdYseUItFodalcpg7eGdsK0OWvEMgocDcGhp5szvpw4BDfvP4UqLhG/rdmbuv6yJYpg4us9cD1lG4EhEfjxjy1ISEyGl7sL/jduIO4/DsbziBjcvB+Ay7ceii+6MpkUH4/pI4JDCkTT69CsFkoV88ZX8/+FRqOFu6vptbQpsLx881GG52X1mmxZ12Efpf7comFNrFr4FZat/8+qbWKWo9Ny8MlsDGXlKSg0Rl3lNEDIJy3BIITH6jOUFFi+3xN4dS6QlEVZieF8Tc/p+CWgStD/fvGBPvgb3w2YnnJephHxiWbWlZJgMBkxH6ECLj0E/jkBnLoDVCoOfDYA+Gcy0PkrIDE569ccEK7PrO68ADwKBVrXACb2AJ7HAB//ndJ2R/1tEXeg6af6xxtqQGPjgf8N4gCU2YRiOjm+j/XErkDLJNksGoDS3IOG4JM8eBKMzi3TCsYpY2ng/zRYBIw0XQwFb4bsInFwUIgEjlRkcUy1bFgNpy/fFUEiueX/LHVZRLQKUbFx8PZwFQFoYlKySZckZWh1mWSFurSqgzlLd6SuN5re+NmQ1WuypEZ1qmDh95OhVCigkMtx8twVjP1kljjfurk6Y92iL1GudAmo4uLx8MkzeLi5oMuwydla97hR/XHp6k2ERcaaXa6QSqGUF+wYNYeU7RlurUlO2Qn6XKJ9L7P9rm1HrRwa0ZPAUzExG0IDfw7NMH//q61N7yv3tj5go+7rE7dN6zXNMdRTbjuXFnwSqquk7vIW1Uwf62DmvOKoNF0Xjao/+o1+pPycraYZ0MPfAG90AH7bnXmbaJvbpwLNpgDn9b152HIGiI7TZ2aX7NdnSQ3bo3ZS8GmMSgUGdQVkPKsFsw1N1Q6YuuUYXNJNxaRKekFN9AtY/JO+Q/PaIgNpTsfmtXH1zmOTQI260ylrSkHcis1HEGN8IkkJIhvXrohZfxmdDIxUq1ASzk4OeGjUBV6hdFG81rc1/LzdcfjsTbNZTEcHBdxdnVGvWlk0qKmfC27fiSs4d80/9TEDOjdF346NRcZ1094zIsDNzmsycFAq8PGY3mbbTcHuL8t3ISt3Hwag4+CJYp84OTrg+JZFGNa3A1ZtPoDZ08dBJpWgVocRcHF2wrHNCxEc8jzL9aW9diVaNqmP96fNyvQxlUoWQ0JcJlOf5DMPJ9NMtDV5u7jAHnhKbD9IZi+hyw+BTl+a3kdzYlLXOAV5xoIi9VMVUc0kjQyn0e3GmVTKUtJ9lCmNiQeeheuXmasRDYkGvFxMu9qpGz49w32GdQ1spp/Hc2u6si3qvo9S6QPnrALQt7vo22MIPg1ofV+9og9QKQBNbXu6wU6EglUakES1qfQzY1bmBCmqwhGJbqYB6IOwMNsJQLu3qSeCvjnLjmRY1rRuJTSsVQE//Wla03Pw1HXxj7rCRw/qgBv3nkJlNOcbBYfBYVF4FpxSq2OkRFEvjBzQFn+s24ckoxHA/k+C8fX8f+Hq7ChqSSuXLYa7j4IyzJlIXfQKhRzf/75Z1Ht+MrYvgp5H4mlQOJb+e1BkVwkFyONf64avfv0nW6/JgLKx3yzciNyioPP3Hz9G+TIlRU2rm6sLGtSuIgLQhnWrY8HSf0TgGxMbhz2HT6Nu9YrZWu/oV3ogJiYG2/ZnfmWDewFBVsmAUvAZFR+PRLXa6hlQCj7DVSqoU2qUbVlkghIymROg5m54ZkMiVcD+K6b3RcTqA8L095MyKUHnpk8zLqO5Ph8uBiYtAX7enhbk0UCf9GjuzlspA34IdanT3KDUw2Y8EKlpZX329E5Kr1pRT/0tZR/To/teNNCvqIf55ypSnidPWUavn/6V9M74WC9Xff0rBdmM2YBnUjU8OtdGyK4XDMLLIYtFGNTtXr9GecxdtgPJyaaXBGxUqwJ6tm+IuUu3I0Zl/k1FQV9ktApVypfAxRsPjLrfq+L4+dsZHk+Dnia81g3LNx0W9Z/mxMYliOwkBYnpA9C4+EQkJCaJrn1CXdG0nrIlfUVbDMEnOXT6OgZ1bQoXJ4fU4Dg7rymvGdCfv5qI5KRkNOr+phjstGvlLJG9zGv937D+XbDn8EkR1GYmWatFch7T67lFwWdeU/t5pUzpdo9PTkYSjWq1cQnJUouXgDBW4A5cBfoZDd4xvhoSdc9/+y9wNaVHi4JGmlOzb2N9PWlYSg9V57r6QPbXnWnP//ekft7NAc3S5gGl59B91IVvqDU1BKI0OOirdabze1LNKdWXGlB3PWVQKZA0ZCrvBAJd6wNtawKHr6c91lBuYPz8dceBSb2ATnWBfZfT7m9YAThwLeOIfcasIAZazHCOREBAsMU/ly0SgHZqUVt0k89btsOkrpNQ8NenU2PMW7oDEdSFkS6IDAzVd5/QiPfSxX0QGJqW6fT1dkfZEr5YuMq0y6MYBZ8jumPllqNi0JGxokU8EBIWJd67FADSIKFTl9LV2KQ4e+U+alYujcNnbohu/HKlfMXAKKrndHFyTA0sKbCOVsWnBp9ZvSZLZkDd3Fzw6HGACD7rVq+AGlUrwv+hvt72/JVbGNK7A5b9sxvOTo7o0q4pQkJfnA6vWrE0ypQqjhETvsp1u5ht4upPZvdoCiT6l9680fqubaqnNPbBUmDvF8Cxb4HFewAPF+DD3vrpjhbtNg1AT97WT3VUo5R+QBBdCYmylV+sTXscBaM0Gv7zwfrufjEIqZj+SkbUbf7XvrTH9m8KLHsPGPUr8PdB/X3zd+rnJqUR9BQAU9DctgYwrA2w55JpvefMDcCQFsCGj4E524Cb14Grp2n0LDB1leX2KWN5ECrV4B+HeDTJh3rkPAegnu4uGNy9uZh2iaYuImq1VnRrkzcHdRCDhMYN75L6nLlLd4hgbkDXpiLwpEFAlL1Zs/04glICUtKyQVWRDaXR7saG9mgBJ0clBnRpIv6RjXvOiO57CoQb1qooBmTQgKYL1x/g2PlbqaPpe3dshPkr9CO/qa5z5IB2aNukhvh999HLopZUqZDjvRHdxAAmrU4HlSrBJAjO6jVZ0ox5y/D7j5/i6oG/ERYeibv302pZJ89YgHWLvsK1gysQFxePx0+DoNG8uNt64uiBuH33Ph48NZ81ZvZLSh+mdlAuwJjFHLoGdJsBzHgV+G44EJcEbD6jn9bIeDwBvS960OCikcDEnvp6UpqDk4JHQ9aTUClX62nA9MFAz4bAq630XeG0TgoKDVnWzNC66HKb3wwDXmsDFPMEnlG962bTQJeERAGtpumnbPqgF7DPUx+AzvgHuJJSI8qYFSVCi9+cYqGVAD3aN8apizctun4J/Bpynt8OUXBMNaJU/+ni7Ih96+Zh576jmPGz/X5zphF2fm5uCImJsYku+JKengiIjLSLLvguiY7YFlcMymQOQBmzR1vck/FBySTMDVCiL1+Kk9mAx1I1qvkEIl6iw9cfvo7P5xhdntYCrD/fDcsVDzdX7F83T5QLyOVyXLp2C98vSPcNm710l+JU8sXNGGOM5ZEWOmxRxovgk/y8ZBMsjQNQOxUWEYV6Xd7IcP/kt4fi9cHdM9z/06KVWL7BqH6JFc5LcfIoeMYYY3kUKNXgG9e0acL6dmmBJeuzmIIsFzgALWR+WrxO/GMvIb4UJ2OMMQs4I09CiDStpIsuEW5p3F/HWCGR1bRajDHGWHYESdSY4mp6gYfg52Yu+JBHHIAyVkgYLinLGGOM5dZ9mQZ35Kaz6ixZr589yJI4AGWsEM2MwBhjjOVWuESDz1wyZjs/G/cKLI0DUMYYY4wxBhp8dFRp2TnNM8ODkBgrBB7J1His0CJOa935U3NCIpFAIZMiWaPl+tV8wPvXvvbx45QBH4+lGlyR8d+L8DFcsPtYotPpaz/NXFbv4Emjy8VaCAegjBUCt+VqvNPVG/uPX4K9sLfJ/u0N71/72sfiQhxww2eukVAp7eeLZH7iY9h29nF+BKDcBc9YIdG2aR1rN4ExxhjLFg5AGWOMMcZYgeIAlLFC4tv5a6zdBMYYYyxbOABlrJB4a1gPazeBMcYYyxYehMRYIeHr7ZHxTqUzUK4pbJFWKoXK2Rla3zhAy5PoWxrvXzvbx4nhQOxToHh1wMEbdivgMqAKt3YrmB3gAJSxQuL+48CMd7abCNTqZXZaDWuj62yEWLsRhRjvXzvbx/cPA0fnAPWHABXbwj5JUPrZPjxZ84W1G8LsAAegjBUSOw6cNr1D4QyUbw7I+G3OmM2TSNNupfb7nnVQKKzdBGYnuAaUsUJi4qh+pnc0HAq42HFXHmPM7jyPiLJ2E5id4ACUscJIIgPq9AZknI1gjBWcmlXKWbsJzE5wAMpYIbHduAu+RlfAycuazWGMMcYyZb+FJowxE45KZdovTV8HlE7WbA7LZ61LAZMbSVHfTwJfZyAyEbgUosOMk1qceJb2OCc58EYtCfpWkqB2EQlclcC9COD3K1r8fkUHrdEltr9oIcWXLTLPS7RcrU5dt25y5h8fex9q0eXfzEeFezsCo2tL0LuCFNV9AIUUuBUOzD2vxfrbfM1ve3bj7iNrN4HZCQ5AGSskOrWqjyNnrgJlGnHt50ugipdEBI+/XdYiSAV4OQKv1ZDiyCsy9Nyoxe6H+kCugifwa0cp9j/SYc55LaITga7lJFjUWYZmJbQYtSstUNx4R4t7ERkDwO9aS+GqAM4Gpd332o6M141uVEyCSQ2l2PMo6yCyeQkJvm0lxU5/Hb45pYNaCwysIsG63jLU8NHiyxM8bZS98vZ0Q4S1G8HsAgegjBU2rd8BHN2t3QqWz/66qhP/jC28pIH/WBkmNZSkBqAUnNZepsGNsLTHUebzr66UhZSKjOn9SP39V5/TP9N1lnLT//vzig7JRnHhqpsZg8x2pSko1mGNmWXGrofpUPkvDR5HG7ddh32DgU+bSPDjWSAuOUe7g9mIYr7euG/tRjC7wDWgjBUSsxb/A/iUBzxKWLspzEri1UBoHODpkDbxa1g8TIJPg0139UFide+sJ4l9tZoEUokEq25mnZVUyvRZzMNPdAiIzbqdD6NgEnwabL6ng6NcggpmrqnA7INOx9lrlj2cAWWskBjWrwMWx7Xn7veXjJtSH/wVcQJeryFFbV8Jvj314iCgmIv+9nl81tnK4dWleBytw5GnWa+vR3kJvBxfHKhm3SZ9MPw8PterYFZ24twNazeB2Yl8DUDlchnGDumI4r6eSFJrEBMbj9XbjiE0XP/Vd2T/tqhYpqhYlpiYjPW7TuJRQKhY1qJBVXRqURvFinhiw+5T2H/yWup66XnVK5VCrEp/lrp5PwAbdutHAPt5u+O1vm3g7KSEQi7H1TuPxfN1OkAiAYb2aIFaVcpAp9OJdR46fd1s22k9owa2g6uzI+ITkrBs02EEhugrW2gddaqVRREvN8xYsAFPg8ykF3Jp+bzPoNbqMPrD72Fp334yGn27t4Onhwf+WLkRX8352+LbYNbjXrQ0EFfV2s1gBWx9bym6ldd3ZiWqdfjtklZ0q2eFBv1QraZ/pM6krjO9Gj5AXT8Jfjjz4qByeA0JEtQ6/Hsnd4OIqIZ1TG0JjjzRibIBZp+a1K+GM0es3QpmD/I9A3r07E1cu/tE/NyuaU2M6NcGc5ZsF79fvPkQK7YcgVarQ+0qZfDW0E6YNmeNWPb4WSh+X7cP3drUM7vevccumwSlBgO7NcPFmw9w8NR1EQBPfac/bt0vLdrQtG5lFPf1wvR56+DkqMT/xg3A7QfPUgNLY8P7tsbRc7dw8uIdNKhZHqP6t8XMxZvFsgvX/bH72GV8PKYP7MnBE5ewefdxfDflLWs3heWDbfG1AFcfazeD5QLl/SiLmR2J6cb+TDmixexzOpR2A0bWlIr1yKUZH2dsfkcpahaRoMcGDTS6rLOfZNUN7QuzsD3LS8SgoqhE5Or1r+ophacD8N6BLBrObB4lfhjLjnw9UtRqTWrwSR48CUbnlnVSf79yK226Bv+nwfByd4FUKhEB6dOgcHG/zniOkGygzCYFl0Qpl0EmlSIqNk783qhWRRw9f0s8Ji4+Eeeu+qNJ7YrYsv+cyTrcXBxRtoQvfv57p/j9wvUHeKVnS/h6u4vs7d1HWaQMLKh7u0aY8cnbUCgVkMtk+O/gSXz09UKxjALplfM/R7GivohVxSEo5DmSkzUY9Nb0TNe379gFcUuv/0UUUimUBXwicUjZnuHWmuRS/Qevk0IBhSybkYEV6RROuIMyaZfzY3alTWkJDg3N3nFWbYkat/WnR+Gy6DTSv6dX3tDgwusyLOsuxeCt5oPGyY0leKuuFP87psGuB1mfC4ZVl+BqqE4MTsoK1X46KXLf/U6j9LuXl2LETg2u6DvBmJ16HBAMF+Mp4V6i87A9kudhH6uSkvK2bRSgDs1r4/Kth2aXdWxeW3SXU/CZvXXVEt304VGx2LLvXGo3+PqdJzH+ta5o27gGnJ0csPPQBTwJ1C/z9nRFeGRM6jrCImNQvrRfhnV7ebiKoNW4LbQdbw/X1PKB7KhSvjiGdG9udtnVO0+wZd/ZLJ9/7sodtOw3DolJyfDydMORjfPRZs9xHDl1Gb/MeB8RUbHoOPQDEYzu++cX3Lxjft/mRqWSxZAQl4tUhgV4ONnO/JXeLimFcjYuyaM0AmWWOemzgncrTIdRu7KX+QvMYoAPjVLfek+HKU0lcJQDCWrT5SNrSvBDGykWXdLi21NZn2tblgTKeUgw5ciL2zW8ugSRCTps98959/vnzSUYX1+KT49osPIGzwFq76IiYuHn5vZSnoftmXcu9vGDsDD7CEC7t6kn6irnLMtYHNK0biU0rFUBP/25LVvrosCNAkRK5NWrXg4TX++O6fPWIjFJjXZNa+Ds1fv478glkcn8cHQvPAwIFXWiBe3Og0B8s3Bjrp/v7emOFb9MR8kSRUXW0s3VFc0b1BABaPWqFfDhF7+KxwWGRuDM+atws+Cb/l5AkFUyoBR8RsXHI1Gd7pPTCt8K6Q0ZrlJBrbWDUZ0xN+AhiUMEPK3dEpYLwXHA39ctE3zRxPM0ap26xY0D0D4VJfizqxQb7+owfl82ajqrS8WUSqtfMKUSDWZqX1qCZdd1SMph7/m4ehJ81VKGuee0+PEMB5+FQelSfnh4MC3R81Kdh+2Q3Ir7uEAiDOp2r1+jPOYu2yG6iY01qlUBPds3xNyl2xGTMqjoRSJj9F3q5NLNh+jfpQmKFvHE42fP0bZJTXz+8zqxLEaVgGt3nqBK+RIiAA2PjBWT5Po/CRHLfWjC3MiM6QTKLHq4OqeWAxDKflIWNCfymgH9+euJeBb8HD1HfgqNRoujm+bDMaW8ID1dShecpSRrtUjOY3o9tyj4zGtqP6+UKV0R8cnJSNLYR01amTv/IqLqaMDB1dpNYQWArn5EUy4Z83DQd4fTqHXjZXTVpLW9pDjyVIfhO7QvPFtQDengKhIcewo8eUEs8Uo1CWRSSaZ1orSuip4QtaHGg4uGVJXglw5SrLyhxYeHOLgoLDw93Sx2/rbH87C9UVpxH+d7AEoj2RvXroh5y3aI0eTGKOvZp1NjzFu6AxFR2R/26Onugsho/ePLl/KDq5MjQsKixO/PI6JRs3JpnLhwG0qFHFXLl8De41fEsvPX/dG6YTWcv+Yv6kQb1a6A+Sv/y7B+ClwfBz4Xg5YMg5BoeznpfrdEBtTF2QlPA0NF8NmhRV2ULV0SgD5ovXXHH2OH98KeI2dF8N20QR3cvGu5Lnhmf9xDLgGlIzgAfUnsGiDD01gdTgfqEBIHlHGX4I2aEpRwBYZuTwvoyrgDW/vJRNBJI9QpsDR2xUyNJ10pqYizBKuOZaf7XYqAGB0OPTEf1pZ0BW6NlmPZNS3e+E/frsbFgOXdpQhLAPY/1okufGMnnunwQH9KZ3bm1v20cR+MWS0ApUBxcPfmInCjrnCiVmvx/e/60eRvDuogutLHDe+S+py5S3dAFZ+I5vWroG/HRqKOk7rZKYu6YNVuUc85akBbuLs6Q0tZumQNFq/bi4RE/WUzlm08JAYMUY2oXCYVA52oS56cunQX5Ur6YsakoeL3fcev4lmwfgQ8TatUt2pZMSqfrNp6FKP6txOlA7TuZZsOpbZxeJ/WqF2ltGjD+yO7i+U0st7Sfly4CjOnjUf/7m0RFBKGR0/SJuJ7//NfseLX6bh+aCVUqjg8CQhCdGzWQfw3H7+Bof26wcFBiSqVyuO1gT0wbPwXOHvplsXbzgpez/aNcPrAWqDdREDhYO3msHy25JoWr1ST4oOGEjF6PCIROPVMh2E7tDhmVHFU3kMCT0d9gLewU8ZBBnTZy6vPTTOQFBAmaXT45wVTKlXx0l9+c/a5F2dVjdXwkcBBLoGfHFjaLWObqB72QRR3ydsjNxcnWG5iQlaYSeDXkN/ldsjJQSnmLqGschEvd+xZOxcLlm3AX2v0I/ftEY2cpOL1kJgYm+iCL+npiYDISLvp+vn6w9fx+c9rgDfXAx7Frd0cxlgOuNw7CL8jPyGkzWSoKrWHvWqpvYjjs999ac/D9kZpxX1s/fluWK5ULFscKxd8CalUKqZoOnb6kl0Hnyzvfl6yCdAkA7f2Ao2GAzxtCWOMMRvFAaidunbnEep1fiPD/TOnjEHPTq0y3D/56/nYc8R0vlNWuPTt0gJL1u8GzqwEanQD3DJOMcYYY/np+LmMF4hhzBwOQAuZz77/U/xjL59ypYrqf0iIBp5cBGp0tXaTGGMvmUZ1quDcYWu3gtkDvmwKY4VE8PPItF+O/gbEvuDyNYwxZmEOFroKEiv8OABlrJBYst5oSrHoQCCMp+VijBWs8MicTVfIXl4cgDJWSHw27hXTOw7PB+KMsqKMMZbPHgfoL/TC2ItwAMpYYRV8C4jlDwPGWMGpV7OStZvA7AQPQmKskDh48rKZO38FOn8MaI0uCm4rJBIopFJx2VfoeDpii+P9a1/7OCYk7fa5P+ySVIaiHjwNPcseDkAZK8wB6OOzwF9DYIt4kun8xfvXzvYxDd5xcwOOLACsfCGOvHhQs6K1m8DsBHfBM8YYY8wivD3drN0EZic4AGWMMcaYRbRtWsfaTWB2ggNQxhhjjDFWoDgAZYwxxphFfDt/jbWbwOwED0JijDFm0xonK+Gttb18CY2A94mVISzBQT8SPg+ipTIEAaijVsA90fZea3qX5EkIlmV8zW8N64Ffl22xSpuYfeEAlDHGmE3brSoOhyTbHMkviZRAp8v75Se369SY4pWMSXFu6BVt+x/Nl+XJaOEdnOF+X28Pq7SH2R/bP8oZY4y91JLlUngl2ehcpqJZkjyvxiFlHXTrbAfVceU1clRXy3FTbjrH8P3HgVZrE7Mvtn+UM8YYe6klq23wQgovuWI6Gb6P9cxw/44Dp63SHmZ/OABljDFm01ycHa3dBGZGw2QlimpMw4iJo/pZrT3MvnAAyhhjjLEcK66T4QsV13yy3OEaUMYYYzZNFZcATwvUWeZJMS/g/Z5A08pAo0qAmxPQbjpw+Lrp45yUwBsdgL5NgNplAVdH4F4g8Pte/b/0o+UlEmByX6CaG/DTb8DM14CjAcDaY6aPWzoBGNUhY7tuPQWqT3xx+10cgW+GAYOaA77ugH8w8MsO4LfdyC0pJOiZ5AQXbSRUUn2N7nbugmfZxAEoY4wxm6aQywFrj4KvWgKYMgC48wy4+ghoUc384yoUA34dA+y/CszZCkTHA13rAYveBppVAUb9avr4b4cBnw0EPpyn//15DLDmQ0CnA9YdN31sQhIwZqHpfVFxL267VArs/hxoVBFYsAu4G5jWJi9XYOYG5FYxrRTvxbvie5cY8bsjXdOesWzgAJQxxphNUyrlQFw+B6AHvwYehgBvzDe//Px9wPt1ICIWGNg88wA0KAKo/QFw40nafb/vAf4aD4zuCMz4B7hPM34CKOENfNQHmL8T+Gs/UBLA7C1Au+bArJHAPydNM6ZqDbDqSM5f24CmQMtqwOj5wNID+vso8/nPx8D0QcCf+4DQqNz9bSDF2HhXzHKOgUYCdGpVH0fOXM3VutjLhWtAGWOM2TZbmIEpNkEffL5IWIxp8GmwKaVrunqptPuom16pABb+Z/rYRbuB0kWA5lXMZzOp+z8nWtfQ36bv1qffnRyAvo2RF35aGYYmOudpHezlwwEoY4wxmxYVo4LdK5YyZdHz6LT76pcHYuOBm09NH3vmbsryCqb3OzsA0SuB6FVA2N/A/LH62s4XcVDos6dJ6aazikvU3zasiLxwhRRTVe7ii8Ksxf/kaV3s5ZGvXfBDe7RAnWplUcTLDTMWbMDToLDUZTUrlULfTo0hk0mRlKzGqq1H8TQoXCwb2b8typQoAp1OB41Gi017z+CW/zOxrFxJXwzt2QJyuQwKuQwnLtzBnmOXTbZbzNcT094ZgKPnbmL9rpPivvbNaqJ1o+riZ1ovPef05Xtm2122pC+GdG8OB3rT6nT4Z9cp3H7wLLVt1SuVQqwqXvx+834ANuy2TNF18/pVMHHMENx5GIQvZv2J/ODm4oifpr6Bbl06Y9n6nfm2HcYYsxR3V2cgOgl2SyEHJvUC/IOAs0afO8W9gGAzXd+BEfrbEl6m9/24GbjgD0glQLf6wPjuQN1y+sFQmiwuBXo7AJDL9DWox2+l3d9a/5mIkt55fonFtDK0TXJAtX4dsHjVjjyvjxV++RqAXrjuj93HLuPjMX1M7nd2VGL04A746a9tCAyJQKWyxTB6UAd8Pf9fsZyCxngqtgZQurgPPhjVEx99v1zUZL/WtzW2HjiPK7cewdnJAV9NHIKrtx8hMDRSPF4qleC1Pq1x8eYDk23Sdn78YwsSEpPh5e6C/40biPuPg/E8Ql84bezdVztj2cbDuOUfAD8fD7H9z39eh2T6Bglg77HL2H/yGuzRiH5tUatWHdy662/tpjDGWLZIKOCyJArGPJwzBomUdPBxM70/PFY/ICgv5o8BapYBenxjGijSiPnE5IyPT0i5j7rHDaauMn0MDVC6Ewh8N1w/sj39gCVjq48Cnw8BlkwAxv8B3H0GdKkHjOuW1o488tHJ8K3KEweK+uR5XezlkK8B6N1HKYXW6fh6u4tpNSgoJPceBcHbw1UEm08Cw1KDT+LkYPrG0KUEsMRBIYdGo4EqPqUbAUCv9g1x/ro/XJwcUx9HDBlUEhGtQlRsnNhm+gDUxdkBri5OIvgkIWFRiEtIRK0qpXHxxkMUFHdXJ0x5ZzC6dGwDBb1OrQ7L/9mFeX+sF0H26/3aYcyIAfD09EREZDRiY2Nx60EgPvg8ZSSlGS0bVEXjBvVw4NhZVCpXIsvtK6RSKGnkaQFySNme4daa5FRnRcefQgGFTGbt5hRKvI/zV2Hav4kxFs5+0oCcQzPM3/9qa9P7yr0NPArN/bZoiqW3ugD/Ww3sumC6LD5JH/Sm55hyn9Fnm1lztwEzXgE61c06AA2OBPrMBFa8D+z9Qn9flAp47y9g+UR9fasFVNLKsfLQVbhYaCR8YTqGbZU8D/tYlZS396VVPumDw6LElS0qlC4K/yfBopveyVEpuuopACX9OzdBw1oVRBD529q9qV9A/954GOOGd0Hfjo3h6uIouu6jqYaGzhOlfFGhtB/mLdspAtHMVKtQUmRPHwZkPKmo4hIRFRMntn3+mr/oji9axBM+nmnfijs0r4UWDaoiPCoWW/adMyktMHBQKvDxmN5mt0/t/WX5riz3UVxCEn76cyO+/Hm1yLxOHNkdg/v1xpFTl+HuJEPfHh1w7up9fPjlz/hodB+89kp/EYBmhto/uGcbuLi6Yee+TaKrPyuVShZDgqE+qIB5OOWwwD4febu4WLsJhR7v4/xVGPavOkhj2SELlx8Cnb40vW/2KP0I9llbTO8P0veu5crI9sAPI4BF/wHf6nv4TFC3evtaGe+nrnnyLKUrPjOUrAmLBbxdX9yWozeACu8Ctcvo60ZpHxi6+GlqKQvw1ErhfukZ/LzSZZHzqDAcw7bOOxf7+EFYxtjH5gNQ6gZfvHYv+nduLAI1/ycheBYSLrJ8BlT3Sf8oWBzYpSl+/HOrqAft1qaeuP/slfsiYP3ozd54FBCK55ExGNarFRav3ZfltksU9cLIAW3xx7p9ovbUnEWrd2NAl6ZiW5Slvf8oCJqUqTC27DsrsqcUENerXg4TX++O6fPWIjFdcXdiUjK+Wbgx1/uI5iYe2LU5hg3qiSI+3pBKJFA6OKBm1fJwUWjgW6QIfli0DlqtDkfOXkfjhnWyXN+bgzuiTt06GDnxG5Qq+uIrV9wLCLJKBpSCz6j4eCRa+drP9K2Q3pDhKhXU6SeOZhbB+zh/Fab96+DoAcRa8JwQqQL2XzG9j0a4U0CY/v7c6tMY+HMcsPG0vtvbnEsPgLGd9SPjA4zKxppWSVueFZrkvogbEGo0sCkrdBxQ4GlAmVOyzzKvOUSmRXiPGgjZk0U29iU9hm2V3Ir72Gp9nXceBGL2g+36Rsik+PHTEald8saoK9zRoSVKFvVGWGSMCPr+XL9fLKPu8wdPQlCxTDFRI+Tt6YoPR/cSyyhzKpFIRKZz2cZD4r7ivp6Y8Fo3LN90WNR/ZoYGQxlnKL+cODi1bZExaZP+Xrr5EP27NBEZ0sfPnls0A9q5RR307d4BR89ex1ez/8KAzk0w+rVBcKCSBK0+42tAwXBWJUqODgpUqVgK5UoVw77188R+USoUaNYI8HB3xaTpGbvtk7VaJOcxvZ5bFHzmNbWfV8qUroj45GQkaaw8AXYhxfs4fxWm/UuDTgHrfinN8bRHaz8EjtwAhs/N/AS95Sww9w19Lea0RWn3v9MFeBoGnLit/5266RWyjF3l0wfrp2X672LafbSvKhbTT1BPGd3MFHEHPu2vD0gtEIBqoMNGpQplqpSGartlzt+F6Ri2VUor7mOrBaBU42joOu/ZrgFu+z9DaHi0qG+k7mL62TDqnUZuPw+PRnxiMpKSklG1fAkxKp3qNcuX8sO+E1fwLDgCk79fkbp+6oKnINQwCp5Gxk8Y0R0rtxwVI9ez27ZWDashKUmdWkPq6e6CyGj9lCC0bVcnR1Enml5eM6AUOMvlcgQEhsLFyQHtWtRF1crlgF1HRFtCnz/HkD4dcebidbRuXB1VK1fEs+eXMs04j5lCkyvPL7DR9owxZilxcYlImcTIuqYN0t/WLK2/HdEWaJUyktzQxV7GF9j6mX7Awr8ngcEtTNdx5ZH+SkokIAyYtx34pD9w/w6wexfwUV+gTU1g2Ny0SehpCqeLs4E1x/SX3iRd6wM9G+rrSrecSVs/jWi/9Suw7IDppPpU83ryNnAvSL++tzrrM6i9vs37ICuqJpBq8I1LNKodOZ/ndbGXQ74GoMP7tEbtKqXFFBrvj+wuAqHp89aJZX06NkLlssVFwEld8Ms3Hxb307RMowa0EzWhWq1WdG1TtzrVRJLf1+3HwK5NIZVJIZNKsf/kVfH87EwJResc0KWJ+Ec27jmDG/eeomyJIujdsRHmr9BPBtymcXU0qVNJ9IMHhUZi0eo9qesZNaCteD3UtuRkDRav2ytel6XR62resCbeGz0A777eD6HPw3H3vv6kdeTsDVQocwijh/fHlf3LERUdg6DgUETHZGOSZMYYszMyuRRIsoEuWLqWurE3O6X9bAhAy/sBnin1dAvfyriOL9elBaBkykogQgXUKQfQZdnpOu3D5wFrjpqWDGw/B3SuA4xsB8ik+kDys5XAT1uyF0DSlZwoGKYAlS4PuvcyMH0N8CDz3sCcOC1PwnOpFuqspoNizIgEfg1t4RoTLIdolL9ao0GyWo13hnXBkP69MWfxWmzdk+5KF3aERk76ubkhJCbGJrrgS3p6IiAykrt+8gnv4/xVmPZvRFJleEZaZ1BkQdninowPSiZhboASfaPNjIy3YYESNdp6heCuXI2vP3wdn89ZbpH1FqZj2FYprbiPrT/fDcuVT97qh769uoosMA3e2rDzsF0Hn4wxxuzTPblaBJ+M5QQHoHbqi5/Xin/GalUujc8mDEPtmik1SSmiVfH48MtfcebijQJuJWOM5V1UtAqe/HFlk8IkGnzmkjZV1c9LNlm1Pcx+8Du6ELl29wmGv/+DtZvBGGMW5Wbvl+IsxJ5JNTiuTPvb9O3SAkvWUzErY1mz4My+jDHGmOXRYFVme2KgxTcuprPAlCtV1GrtYfaFA1DGGGM2Ta3mASi2KESqwb8OpvNSBz/Pw5Wj2EuFA1DGGGM2LSGRu99tTSK0+M0pFtp0yekl6/XTGTL2IhyAMsYYs2muLk7WbgJLJ1iqxQLnjHNPfzbuFau0h9kfHoTEGGPMpsW5KBBsgxfaoOSfXCYTczLrLHAlIcPtbf3VEW2WRAescVQhnn5gLJc4AGWMMWbTelVJxMXkQBTmSbzFhTjghsmukVAZjSq3NwdPXrZ2E5id4C54xhhjNs3b083aTWDZxAEoyy4OQBljjNm0tk3rWLsJjDEL4wCUMcYYY4wVKA5AGWOM2bRv56+xdhMYYxbGg5AYY4zZtLeG9cCvy7ZkXOBXBVBYb4omrUyGBFdXaF1igTwOQoJaBagjAK8ygNwFdifiMRAXYe1WMDvCAShjjDGb5uvtkfHOIhWAwb8AUut9jKkBBEkl0GktMB3Rg6PAyflA4+FA+dawKxIJ8PA0sHWqtVvC7AgHoIwxxmza/cdmpmBqMx5w9oS1WWwmTIVj2q2jK+yOhCv6WM7wEcMYY8ym7Thw2vQOlyL67nfGmN3iAJQxxphNmziqn+kdLccAbr7Wag5jzAI4AGWMMWY/FM5A+ebWbgVjLI+4BpQxxphN227cBd9wKODinS/bKeYCvN9AiqbFgUbFJHBTStBunQaHn2Ss9OxcVoKh1SRoWlyC6t7Akxig/B+aTNf7VQspOpeToJgz8EwFbLmnw7entAhPMN8WuRS4PFKGGj4STD6kwexzL642fTBWhnIedIV6U79d0uLdfdoM93csI8HUZhI0LCqBVALcCQd+PKvF+tt8jXeW/zgAZYwxZtMclUr9DxIZUKc3IFPky3aqekswpakUd8J1uBoKtCiZ+WOHVZdgaFUJLoQAz2Izf5yLAjg5TCZuF17S4UmMDnV9JZhQX4L2pWVouEJjdiDTe/UlKJOLK5BeDNZh9jnTYPNORMYtjKolwV9dpdj7UIepR7XQ6ICqXhKU5quesgLCAShjjDGb1qlVfRw5cxWo0RVw8sq37ZwP0sF7vhoRCcDAKhK0KCnL9LEUtI3dA6i1wLb+UtQqkjHzSPpUlIisZM+NGuz0NwSCOoQnSPFFCynq+gGXQkyf4+sMfN5cih/OaDGjVeZtMCcgVodVN7POYJZ1BxZ0lOLXCzpMOmgcrHLmkxUcrgFljDFmH5qOAJT5N/F8bDJE8JkdgTRvfMZe7QzcHfS3wSrT4C4w5fd4mkw0ne9bS3E7Alj5gkAyMwop4JxFkvidulLIJMDnx/UvgLKzjBU0DkAZY4zZtFmL/wHKNAJcfGBvjjzVQaPV4ecOMlFbWtIV6F5egmlNpdh0V4vb4aaPr+AJjKwpwaQDGuhyEX92KCNB3CQZVO/LRU3oxAYZM7OdykpwKxzoUUGCJ2/LEPu+HGHjZfi6pRTm87iM2WAXvFwuw9ghHVHc1xNJag1iYuOxetsxhIZHi+Uj+7dFxTJFxbLExGSs33USjwJCxbJ+nRqjfo3ySNZooNVosXnfWdy49zT1wgpDe7RArSploNPpsP/kNRw6ff2Fywits3eHhqm/L1j5H8IiMy/SaVG/CkYOaIeFq3fj8s1HJsuqli+BSaN64N//TontGF5T9UqlEKuKF7/fvB+ADbvTzVOXS3O/nIBypYqi/5jpsLRFMz9Em+YNoKH9rdVi0bJ/sXjVDotvhzHGLGlYvw5YjP6Aozvszc0w4K09WvzUTopTw9M+cpdd02LM7owp1JE1pVh3W4dTgfqu8py4EqrDsQCdCGp9nIBRNaUi8C3hqsWUI2nbquwFaLTA0m5S/HhWh8shWgyoLMH05lIx+InKCxizixrQo2dv4trdJ+Lndk1rYkS/NpizZLv4/eLNh1ix5Qi0Wh1qVymDt4Z2wrQ5a8Syu4+CsOPQBSSrNShVzBuT3+yDT35ciaRkNZrWrYzivl6YPm8dnByV+N+4Abj94BkCQyKyXFa6uI8IbOcs3Y6omDg4KBXQ6jJ/M/l4uqJVo2rwfxycYZmjgwL9uzTBtTv612Zs77HLqQGpvTh57iqmzFyMqGgVmtWvjrWLv8XOA6fxJPC5tZvGGGOZkhetBCSUyNFzKJOnzGb5ZGIeL+P+IgGxwJlAHXY+0OJRNNC6lAQT60vwPF6Kjw+bfj6Vdgc+MgoWc6LvZtPnLb2mwa6BUnzYUIJfL+jbQVwVgEwqwadHNPjxjD7NuvGuDt5ONAuABN+d0pcjMGbTAaharUkNPsmDJ8Ho3LJO6u9XbqVlFP2fBsPL3QVSqUQEpNeNnhcQrO+HcHNxFNnKRrUq4uj5WyLDGRefiHNX/dGkdkVs2X8uy2W07X0nrojgkyQmZf4uokwqBctrd5zAoG7NMix/tVcr7Dx8EfWrl4M1NKpTBQu/nwylQgGFXI6T565g7CezRLeMm6sz1i36EuVKl4AqLh4PnzyDh5sLugybnOn6lm/Ym/rz6Uu3RMF5+dLFzAagCqkUSnnBjlFzSNme4daa5FJ9dYoT7XtZzgYBsOzhfZy/CtP+/SemFuCZs6mX2pSW4NDQ7L3uakvUGbrCLaVFCWD7ACmardLgfEqeg6Zgik6UiEFIS67ps6SOKae9HfeBpxZMQM49r0O38lK0Ky1JHZxEdaeuSmBNuhpT+r17eSnqFwWO6jsjs00mlabNVmAhhekYtlXyPOxjVVJS3rYNC+vQvDYu33podlnH5rVx9c5jEXym16J+VTyPiE7tKvf2dEV4ZEzq8rDIGJQv7ffCZZQZDYuIwUeje8HRUYmrtx9j24HzIlhNr1OLOrj/OBiPn2UMwBrULC+eQwG0uQC0Q/NaaNGgKsKjYrFl3zk8DQrL8BjKvn48prfZfREdG49flu9CVu4+DEDHwRMRo0qAk6MDjm9ZhGF9O2DV5gOYPX2c+AZbq8MIuDg74djmhQgOyX4mc9KYQYiNVeHURQpEM6pUshgS4hJhDR5O+TfIIKe8XVys3YRCj/dx/ioM+/eJLOdXPboVpsOoXdlLbQZmMY1SXr1dV4pgFVKDT4Ot93X4qqUELUpIcDNMh54VpTh6ADgZqEXZyvrHlEqZEsnLUYKy7jox3VNyDoNTmvaJeKdcap7Qeqp4A8H6PE2qkJTfvRwof5yzAlQHhRx+bvkzh1NhOIZtnXcu9vGDsIxxj9UC0O5t6sHP2x1zlh3JsKxp3UpoWKsCfvpzW4Zl1SqUQK/2DTHv77zXI1J2tXTxIiK4k0gkGD+8K9o2qWFSI0pK+HmhQY3ymPXX1gzrcHd1Qo+29TE7pYwgvS37ziIqNk5kIutVL4eJr3fH9HlrkZhkOpyRsq/fLNyY69dCQefvP36M8mVKimDYzdUFDWpXEQFow7rVsWDpPyKYj4mNw57Dp1G3esVsrbdv15Z4d9RAvDX5O1HuYM69gCCrZEAp+IyKj0ei2ny7CvJbIb0hw1UqqLVcD5UfeB/nr8K0f6sn3cBVTRFAlv1zEgVXf1+3/rRCRZ0pO2h+pDqhmktSJCVAnNVWhr69TF/ntGZS8a/e32pc1g+hyLYKKRPTh+qHLAjng3Wo4i0RA6IeRKXdX8JVfxsan/P9lpisRkhMWmLIEgrTMWyr5FbcxxaLMKjrmwb/zF22A8nJpt86G9WqgJ7tG2Lu0u2ISRm4Y1C5XHGM7N8OC1b9h+Dnae+E8MhYeHu6wf+JfoI0H083RKRkR7NcFhWLizceirpScvHGA1QoUzRDAFqpbDH4eLlhxqRXxO8erk4o3qcNPFzPiXV4uDnjf+MGimWuzo6oW60sXF2cRPAZmdK9Ty7dfCjqRIsW8cyQSc1rBvTnryYiOSkZjbq/KQZw7Vo5C44O5rs4dGayyub07NgMsz5/Dx9Mn4NDJ69k+rhkrRbJeUyv5xYFn3lN7eeVMqUrIj45GUmafC4Qe0nxPs5fhWn/9vb0x9W4WoCbvqfLntyJALqWl6BtaYnJFZVerSZNnTie/PdQ/+FPk8gvTfn88nMGfu8iw9JrWtFtbwgWKWit6AlEJQJBKv19Xo76340/CuhxU5pIkajW4eDjtAU0yOnV6sCbtaX43zH9dilMfaOWFGHxugzZ2uzQaLUWP28XpmPYVimtuI8tEoB2alEbjWtXxLxlOxCfYHoAUtazT6fGmLd0ByKiUt4pKSqXLYbRA9uL0edPg0wLcM5f90frhtVw/pq/GGjUqHYFzF/53wuXnblyD3WrlcPJi7dFBpRGq99/FJShzUfO3hT/DD4c3Qv7T15NHQX/8Q8rU5fRqHfqYjcMOvJ0d0FktP61lC/lB1cnR4SEGX2NtFAG1M3NBY8eB4jgs271CqhRtSL8H+rrZs9fuYUhvTtg2T+74ezkiC7tmiIkNOt0eI8OTTH360mY/OU87DhwJtftYoyxgnTkyEnAtxhQq1e+b2taM33GsKaP/nZEDQlapVwR6dtTaUFc7SJAn0r6x1TylMDDIe25l0OA7SmTzs+/qMUbtSRisnqa+P1RtE4Eo8OqS7HnoRZnUj6eHkUBFF5fCNJBdU//XMMo+OvPdSIANaDM5a3RcjGS/o3/tKkT3v+vmRT/3qFAVSe63GkbtX0l+OyIxqS7nda175EWnzWVoIiTFJdDdehXSSIGR721R4MkjvWYPQSgFIwN7t5cTLtEQRxRq7X4/vfN4uc3B3UQ3dXjhndJfc7cpTugik/EiP5tIZdLRYBnsGTDQTwLjsCpS3dRrqQvZkwaKu7fd/yquJ9ktezc1fsoU7wIvnhvsOievvcoCPtP6QPHOtXKom7VsmJUfl6MGtAW7q7OYiojyvYuXrcXCYmWHzI4Y94y/P7jp7h64G+EhUfi7v20AV2TZyzAukVf4drBFYiLi8fjp0HQaLLutv5x+gTIZTJ88+k74h/57LuFHIwyxmyamuYMOvY7UK4p4JrzetCc+CbdlYcoS2jw7am0c2yDopIMjzX8ToGhIQClDChdbvObVlK8VkOCYi4SUYM566wWX6RMBG8JV5/rcCNMJ7bh6yRBklZ/haXBWzUiKE2v32ataBNdTnRUTYmY+H74Dg1W53Lye8ZySgK/hny02SGaf5VqRKn+08XZEfvWzcPOfUcx4+dVsFcuSqUoYqc6Ilvogi/p6YmAyEju+sknvI/zV2Hav19/+Do+n7McGPyzPggthFzuHYTfkZ8Q0mYyVJXaw+7cOQRsmWLRVRamY9hWKa24j60/3w3LFQ83V+xfN08MupLL5bh07Ra+X7DW2s1ijLH8c3gB4FcFcM6/68EzxgoGB6B2KiwiCvW6vJHh/slvD8Xrg7tnuP+nRSuxfMO+AmodY4xZzs9LNul/CLkDRAdxAMpYIcABaCHz0+J14h9jjBUWfbu0wJL1u/W/UC1or68Bx/yZc5IxVjDMzE7GGGOM2Y5ypYqm/fLgJBCnH3TKGLNfHIAyxhizacHPI03vOLsaSE6wVnMYYxbAAShjjDGbtmS9fp7nVNe2cxaUMTvHAShjjDGb9tk4/RXrUmnVwI3dAE/Nw5jd4kFIjDHG7M/ZlUDVDoDEinkUif5a2uIa2nmdUTsuPO024insCv0NIvRX6WMsuzgAZYwxZtMOnryc8c7EWOCvISg0k3grlXT9ZeDgz4CVL8TBWEHgLnjGGGP2F4AyxuwaB6CMMcYYY6xAcQDKGGOMMcYKFAegjDHGGGOsQPEgJMYYY8wGldHI4KO1rTzRY5kGYVKttZvBCgEOQBljjDEbdDCiKNx1EtiSAKkG9byDxBRUjOUFB6CMMcaYDVJLdCiita2PaYkWaJmsxHElTxXF8sa2cvuMMcYYs1k+Ohlmqjyt3QxWCHAAyhhjjLFsq6yWi3+M5QUHoIwxxhjLtmI6Ob6P5SwoyxsOQBljjDGWI03USvja2Ah9Zl/46GGMMcZYjhTXyjBN5V5g2/s92hu6kDLYFumbYZmDDpiicsf1sOJQhZTC0+clsD6qCGqoFSaPa53kgC2RRfD4eQnEh5RG4POS2BXpixZJymy3o4RGhnVRPogILYWo0FLYHFkE5TUyk8eU0sjwucodp8OLIjy0FEJDS+JghB86JjnkYQ8UPhyAMsYYYyxHZJCgf6IznApgmqgGSQqMSnBBPMzPP7oqugi+VnngkDIBE90isNgpFm2SHHAyoqiYS9WgikYu1vCbUyzGu4XjJ+doFNPKcCSyKLomOr6wHS5aCQ5G+qFtkiO+c47CFy5RqK9W4nBEUXgbZYP7JjrhU5U77snU+J9LJGa4RMNNJ8W+yKIYFe9iob1i/7iKmDHGGGM55qeVYlycK2a7xOT4uZQRfChT4w338KwfqAPmRHtguaMKHZMczWYkByY6Y5ZzND5xjUy9/6giEQcji2JAojPmOevb95eTSvwzttApFv5hJTAp3g27HRKybMq4eFdU0SjQ2CsI5xT6aah2KeNxLbw4PopzwzTXKP1rUyaiTJFnJhP2/+YUg0vhxUWgvCxdG15WnAFljDHGWI45Qop3410h1eXfNvpHyVFTrcA0l7Tg0phbSgY2WKoxuT8w5fd4SdaNo+WhEg08s1HPOijRGWfkianBJ7ktV2O/MgFDEp1T77shT85wtagkCbBTGY/SWjlctTyLv0UyoHK5DGOHdERxX08kqTWIiY3H6m3HEBoeLZZ3b1MPzepXgZ+3B35buweXbz5KfW5Wy6a81Q9yuf6AkEqlKFnUG1/P/xcBweHw83bHq71bwc3VCTKpBDsOXsC5a/4m7XJzccTnEwbB/0kIFq3eY7bt9Jg3BrZHEW93qNUarNl2DHcfBYllZUv6Ykj35nBwUAA6Hf7ZdQq3HzwTy2j7r/VtA2cnJRRyOa7eeYwNu0/Rw/LsuylvoX7NCug+YgosbdX8z9G0YS3EqeLE7w8ePUXfN/9n8e0wxhh7OfjqKAPphH8c4y2+bgrUPn2uwA+uMQiWme9+vy9T44lUjY/i3HFbpsZFeRJKaGX4MdYT/lI11jpkzDa6aSVQQoIiWileT3BBbY0S3zrrs5eZoTi2jlqJJU6xGZadkSeha5KTaG9sFtE4dferoEXcC4Lil4VFuuCPnr2Ja3efiJ/bNa2JEf3aYM6S7eL3m/cDcPbqfbzev22G52W17PvfN6f+3KBmefRq31AEn2TkgHY4cfE2jp+/DVdnR0x9tz/uPQpCZIw+sCLD+7TGlduPxfLM9O/cVASovyzfJQLOd1/tjKlz1kCr1Ymfl208jFv+AfDz8cAHo3ri85/XIVmtwcBuzXDx5gMcPHVdBOBT3+mPW/dLp+4DW3b0xFm88dEsazeDMcZYIeCuk2K6yiNfAtBpsW5IkAK/uMQik/JPqCXAQI/nWB3tg21RaQOUzskT0cIrCFFmAsL10UXQLclJ/JwIHX5zjMEMl6wDUG+dFI6QpGZWjRnuo8D3jlRt9vkV1XIMSAnUOQFqoQCUMofGgdeDJ8Ho3LJO6u8PA0IzfW5Wy4y1bFAVx8/fSv29VDEfXLuj32ZsXAKeBoWjUe2K2HfiaurjwyJi8DQ4HPWql8t0vQ1rVcD0eWvFz48CQkUAW6VcCTwJeg5XFycRfJKQsCjEJSSiVpXSuHjjIXQ6HZwc9aPmlHIZZFIpomLTgl9LqVGlHJbOnQoHpUJkWs9fvo4xH/+EpGQ1XJydsGbB56hUoTTi4uJx/2EAivl6oe2g9y2ybYVUCqW8YEuEHVK2Z7i1JrlUn313UiigkJmOcGSWwfs4f/H+ta99bO78J7GTC66X1MrRUeuCU47JZpfLdYBHuqjLQSKFi0SGMjLTkeHhUh2oV71SsgwTVK6YVDIRMpkCLloZJBKJ6PV0UZqOWk+UyHBNqcFmeSLOKpNRQS3D5BhXbIzxQ2/fcCSm241feaqwQBsvRqsPVznBWSqDh8IBqiyylz5q/d9aJ5dl2L4uWf+395Yr4aLM2JXvpAU2RPggXgJ87aWCiyz7o+5t+RhWJeXtcqwW/6Tv0Lw2Lt96aLH1ebm7iKBwyYaDqfc9fhaKpnUrY8+xyyji5YaKpYuKgJP4eLqhTePq+OmvbSIozYyLkwNkMimiY9O+tYVFxsDb01UEnlExcSJAPX/NX2RHixbxFOsm63eexPjXuqJt4xpwdnLAzkMX8CQwzOx2Jr/ZG47UjW/Gt4s2iWA2M4+eBqPbqx8gIloFRwcljm5eiDdf6YpFK3bguyljRQlB/c5vQC6X4+imBYiJeXEheMumDXF1/9+IionF3N/XYMPOY2YfV6lkMSTEJcIaPJz030xtgbcLj1jMb7yP8xfvX/vax8bnP1mwFMiYcLM5lB18ResGfzfzAWhTlRRrAsyf1wfHm97fumIcApQ6zHvsgAvOWvznroE39PtXFiwRAbqfm/6zmLhpgH33nfC7TzL+8qFIU4mzlAxTJWLtYyeM03hhlZdpVjI45d91AAd0ydj6wAnLYpQYXyrzzzxXWkUQ4K1wgJ+baZDpm6wPpZxdneFHc0IZoZj2t6cOqKaWYXTpBGhdXOAH25ObY/hBmPm4xyoBKNV0Un3knGVHLLbO5g2q4MqdR1AZBUPLNh7CoG7N8b9xAxAWGSsCRq1Wn58f2b8t1uw4LrrK82LR6t0Y0KUpurWph8CQCNx/FARNyjbaNa0hSgf+O3JJBIEfju4lsrlUUpAeBcK5RVnWP378GJUqlBGBqpurC+rWqCyWNWtYG+s27URiUrL4t23vMbRrVjfL9U3/8Q88DQpFUpIar/briB+mT8TNO49w417G0oF7AUFWyYDSyTcqPh6JavPdGAX5rZDekOEqFdQpf3dmWbyP8xfvX/vax+bOfxqtg12MFaYu6DnKKITEmP/cPaqVoFcR0y76mVFuCJZqMc/NtEbzRkISmkUp0U7lgteKRKJkkhJR8XFQa3WQaB0hTdbAMSIOEVItYqQ6dFU5wVcjxXpJtMn2qQgwSuKAmtFahMizTs5sVerwUYwLoqNjkJBJ0jlUByTAGW7xaoSkS/a4xLnSXxDXE2IQm2QagC4Md0eHOBne9I7CFm0CkPMJAwrtecJiEQZ1u9evUR5zl+1AcrLlvrK1qF9VDGoyRkHn4rV7U3+f+Hp33Lj3VGQaSxbzxtghncT9jkoFlAq5qN+kdhlTxSeKoNXd1Sk1C0oZzvBIfYExdetTbajBlxMHi0CUtG1SU9SDkhhVgigHqFK+hNkANC8Z0NlfTIBCLkWzXmNFAL5l6UzRHW9OdgZA+T8OTP15zeb9+HTC62jRsIbZADRZq0VyHtPruUUn37ym9vNKmdIVEZ+cjCSNHaQg7BDv4/zF+9c+97Hx+U9HcxDZgeuyZNzUxgOZnLYpxAyQmmYXJ8MZTyVq7JCmG9STDPgm6j/nVj73BJ7TT2kjzEtqZLgR5ItJrhH42TkGnilTMyUmJ0OlM0pc6GiuUhHFv/DzRK7WQgoJpIlqqNKNXjd2VZ6EuomyDOurnyDDfWkygtWmr5EGQr0e54z3XSOwjIJg636s2dx5wiIBaKcWtdG4dkXMW7YD8QmW28PVKpQQ9ZU37z81ud/NxQmxcfEi6KpRqRSK+3rhzJV7Iuv50czlqY9rXr+KqAHNbBQ8da+3aVwD2w+eF93snu4uuPNQP9LdODBt1bCayBre8tcvex4RjZqVS+PEhdsiwK1avgT2Hr9idht5yYB6uLkiLCJSBJ/VK5VF3ZpVcfDoSbHs9IVr6Nm5NRYs3waZTI5enVtCFZtxdJ6xqhVK4ba/fl+2alIH7q7OuHDtbq7bxxhj7OVGUxh96qJPzljKAWUC+nmEirEIhuwcJUXoakiPZGp86xKNqzJ9d/+dlNtXElzwVco8nKQPjUrXScWoeAO6dGhougCTalNpHtHHUrXJstIaGZx1EjHNksG/DnH4QeWFhslKnE+ZiqmKWo4OyY5iUntjk1Vu+DjOXYyu/yVlHlJm4QCUgrbB3ZuLaZeoK5qo1drUUew92tYXNZk0qKeknzeSe6rxzcKNYvBQVstIy4bVxGj39Nm9OtXKoFvretDqdIiKjsOvK3Zlq8u9bIki6N2xEeav+E/8vnHPGYwe1B5fTxoKjUaDJf8eECPgCbWrSZ1KgESCoNBIkyCWSgBe6dkSHZrXglwmxZVbj0SXvKV9PXcpls6dhqsHliMiIgq37z1IXTb1+9+xZuEXuLh3GeJoINazIDgosi4gXvT9x/At4gWdVgu1RoNZC/7Gheum01cxxhhj2fVEpsEFZbLF1/lEFi+ycyXdHBGgSRDZuXkSnei23+KQ1p2/zSEe12RJ+DzOHWW1MpxSJKGSRo4Jca54JlWbTDy/K9IPT6VqnFYkIUSqQRmNHG8kuIjR60PdRao11fJoH7RLdoTE77HJpPVjE1yxI9JXBJzJEuDDOCol0GC2UZDZL9EJs1ReIji+KU/G8IS0DC7Zq0xASBaZ1peFBH4N7SPHz0zQ9E9Ojg6IiY0Tt3vXzsXx0xfw6cw/Ya9oZCEVl1N9jS10wZf09ERAZCR3X+YT3sf5i/evfe1jc+e/22HFxZV3bFWURIPX3MKw3THrKwjl9kpI6ffvg+clcE2ejN6epjPoeGolYiqonklOKKuRI0aixT5lAqa6RuKhLO3vQldteiXRGdXUCnjqpIiQaHFKkYhZzjE4pkzM0L70AaihBGBurBe6JNE0/MAhRSI+cI3AfaNM6RexHvgyziPT19XOMxiH023vZTxPWH++G5br7vnda+aIaaBoFPy1W3fx9c8rrN0sxhhjLwnKRu54weUrM9PeKyTHzylfRF8Gl16kVIeP3CLxEcxfLclgoXOs+JeX9gXINBjiYZotTY9KAYzLAZh5HIDaqbCIKDTq9maG+8eP6o+3X+ub4f5Ff2/AohW5r0dljDHGDOKhxTynGDFnJ2O5wQFoIbNg2SbxjzHGGMsvVMP4l5nLUjKWXbY/wRhjjDHGbIYaOqxxVCGJs58sDzgAZYwxxliOJp7/Id20Q4zlFAegjDHGGMu2I4pEMfCHsbzgAJQxxhhj2RIoUWOaa9ajzRnLDh6ExBhjjNmgKIkWD6SWneQ9ry7Kk/HIaG5NxnKLA1DGGGPMBjXxDrZ2ExjLN9wFzxhjjDHGChQHoIwxxhhjrEBxAMoYY4wxxgoUB6CMMcYYY6xA8SAkxhhjzApctRKU1MrEaPcYazeGsQLGAShjjDFmBX/EeKNjkiOSJTrUdXxu7eYwVqC4C54xxhizAkedBL46GXy1MvRMdLJ2cxgrUByAMsYYY1akgAStkh2t3QzGChQHoIwxxpiVuesk1m4CYwWKA1DGGGPMyhyQEoDqrN0SxgoGB6CMMcaYjairUVi7CYwVCA5AGWOMMRsxKME5X9ffIFmBbZG+CHxeEjGhpXA5rBjei3OF1Cjz2jbJAbqQMpn+m6pyz3IbxTUyrIjywa2w4ogOLYWI0FI4HV4Ur8e7vDDDuyfCV2zj1xgvC71iZqt4GibGGGPMRvhopaibrMBlRXK+BJ8nIorhriwZPzhHI06iQ/dER/wS642KGgUmuUWIx92UJ+M194zTQo1IcEHXJCfsUSZkuZ0iOilKaWX41yEOj2UaKHRA5yRH/B3jg6oaOaa5Rpl9Xv8EJzRXO1jo1TJbxwEoY4wxZiM8IMUPsZ7o5hWa4+cejPDDQ5kab7iHm13+drybuG3jFYIIqVb8/LtTLA5F+GFUgktqABoi1WKVY1yG53+h8sAdWTLOKZKybMdVeTLae4WY3LfAORZbI30xMd4N012ioE035spBB8yO9RKB8QyVZ85eOLNL3AXPGGOM2ZDaGgVKaWT5MtI+QaJDpEQffBoESjWIl2TdN944WYnKGgVWOapyvX0Kjp11EigNA66MfBLnLgKSn5z5mlAvizxlQOVyGcYO6Yjivp5IUmsQExuP1duOITQ8Wiwf2b8tKpYpKpYlJiZj/a6TeBSg/1bXr1Nj1K9RHskaDbQaLTbvO4sb956arN/NxRGfTxgE/ychWLR6j7ivUe2K6Na6LqRSfex84sJt7DtxVfzs4+mKkQPaoUzxIngeEY1vFm7MtO1Zbb9dkxpo06QGtFqt2M7Rczdx8NT1lDY5YXifVvD1dodMJsXRszex/+Q1WAK1f82C6Th56S6+mPUnLK1EUS98P2UsGtavDZ1Wh8iYOHQc/B4Skyzf1cMYYyx3SmjlmKHyyDSTmVuHlIl4JdEFi2O8McfQBZ/khAGJzvjYNTLL5w5PcBG3qxwyZkazmmjfRSeBq06CtkmOeCPBBScVSSIINlZaI8MUlTtGu4dnWMYKrzx3wVMAdu3uE/Fzu6Y1MaJfG8xZsl38fvHmQ6zYcgRarQ61q5TBW0M7YdqcNWLZ3UdB2HHoApLVGpQq5o3Jb/bBJz+uRFKyOnXdw/u0xpXbj+HqnDZBb0RULH5ZvgvRsfFwdFBg2rsD8PjZc9x5GIj4xGRs2XcWTo5KEWBmJavtn7p8F4fO3BCPo218MWEw7j0KwpPAMAzu3gyBIRH4bc1eKBVyfDK2D+49Dk4NrG2VQiHD95++iaLFiqNZr7cRq4pHqeK+4vUzxhizLXSJTnetBNHGo4Py6A/HWNRUK/B2vCvGJriK+9TQYYJbBBY7xWb6PGrC0ERnnJYn4r487TP6Rd6Pc8P3Rt3p+xQJeMM9LMPjqOv9oiIZ68x0+7PCK08BqFqtSQ0+yYMnwejcsk7q71duPUr92f9pMLzcXSCVSkRAet3oeQHB4akZz7BI/ZugZYOqCIuIwdPgcNSrXi71sfcfB6f+nJCYjKDnkfDxcgMeBiIuPlEsr1Ku+AvbntX2ab0GFGRSptOgVDEfHDqtD04pWL37MAjN6lbOlwD0s3cHY2CfrlAo5JBIJFi5cQ9+XLBKLOvcsh6+mPwmvDw9ERoeBQc5cOryPXzw+Tyz62pSpxIqV66EKd8tRkys/k3+5JlpjY4xhVQKpbxgS4QdUrZnuLUmeUqG3UmhgEJm+a4wxvs4v/H+tf19bHi+OcW0MnyW6IVvPMwHhnId4JGukNJBIoWLRIYyMtOBPOFSHQzz3D9J0mGfLhGbnBKQKAEGxzmKEeeRSgm2OyWa3VaHBKVoz2w3FVyUymy/vi3SJFxzCoevVopu8Q7w00rhI1ciQpGW+GiToMTARCe08wszWTd9BtF+JXwM2+YxrEpKytu2YUEdmtfG5VsPzS7r2Lw2rt55LILP9FrUryq6zA3Bp4+nG9o0ro6f/tomutwzQ13/FUoXxaqtx/LU7vTbJw1qlkfvDg3h6+2BzXvPiOwnoWwrBXMPngbDxckRNSqXQvBz8yP6qDyhaBEPs8sWrNyNiOisa2mW/LMXs//cJALdXh0a4ouP38W+I+cQFByCT8cPx+WbDzF+6mwM6t4c33w2Ict1lfD1gm8Rb7w1rDvmfDEeGq0OS9ftxKK/N5l9fKWSxZAQZ/5klN88nGznmsjeLvpuJ5Z/eB/nL96/truPlZHyLC/P2T/RGb+7mb9CUlOVFGsCzJ8rB8eb3t+6YhwClDq881yBUbFydKgUjzipPrg7Bg1WPdJiXpQHzvvGQ2NmcyNjlCJTethPBj+5fiBTdtAniD5dAxyGFt8GyrEzzAcdK8YjUQrIdMDcUCds8lDjqY8T/Iye66RUwjtlt/IxnP9ys48fhGXMZlslAO3eph78vN0xZ9mRDMua1q2EhrUq4Kc/t2VYVq1CCfRq3xDz/t6Reh/Vjq7ZcTzL7mFPdxe8O7wrVm09isgXBHJZMbd9cuH6A/GP6jLfGdZFBM8UaP7z30kM6toM08YNFDWvdx4EisypOX+s34+8aF6/KiaOfQW+RXzE7y7OTqhZtTw8XOTw8PLCitl/i/v3Hb+Ccc+eZbkuqmWVy+R4HhmHxj3ewqu9W+HjCaNx1/8p9h09m+Hx9wKCrJIBpeAzKj4eiersd/Pk17dCekOGq1RQa00L9pll8D7OX7x/bX8fJ6kp42T+PBsHLRY6xyAkJt7s8qNaCXoVMV02M8oNwVIt5rmZfibeSEhCYiLwargvDioT8VBlOtBns0KDH+Lc4RQZB3+56eeuI02hFO2Hgw5JuBGftwFCa+RKvKr2RpWwROx3TMIIlRPKJ0owzj0ajhHG+88FskQ1XKPioXZzwrN4PoYL43nCIhEGdbvTgJ65y3YgOdn04G1UqwJ6tm+IuUu3I0Zl+mapXK44RvZvhwWr/kvNIlLNZcli3hg7pJP+d6VCdIN/MKqnWD/xcHMWv+88dEEEibllbvvpUVb0wZMQUcMa/PwqVHGJ+HvT4dTlw3q3wrMQ/dQVlsyA0uCt98e+gpMXb2H6j3+gZuXSWDjzYzg4mOn+0L24Rig8KhYRkVFYv/WAGFx14ORVjBoaggZ1qpoNQJO1WiTnMb2eWxR85jW1n1fKlK6I+ORkJGm4TjY/8D7OX7x/bX8fZ/WBHyrV4jdFFDI7FdKnR4DUtJdqMpzxVKLGDmm6bvuUqjI/jRQ6rTbD+VWXMvdmUlIyVFrTL/89E5zhrpNiuTImz+dlSbJ+fzkkaaGSJqFoopMYEb8/VJ9kMTY8zkn8e7tUApZp+BgujOeJPAegnVrURuPaFTFv2Q7EJ5genJT17NOpMeYt3YGIKNNgq3LZYhg9sD0Wrt6Np0FpI/2o/vKjmctTf29ev4qoATWMgnd3dcIHb/TE7qOXcOrS3Vy3O7PtG7r2A0P1IwJpABRlSS/e0Ae6Lk4OiE9MEqUEpYv7iLZ9s3CDxTOgtB25Qo7HAfo6zX6dm6NUyRLi59sPniEyIgLD+3fGqfPX0KF5HRQvXhyX7wRkur5z1+7j0ePH6NSmEY6fvYIGNcrDy9sbN+7kPoBnjDFmedTdvdJRhSTzve+5RnN40oTw3lopwlPmAaUBRkMSnREt0eK+LGPP07AEZ6igxSYH85lYGihVXCsTUzkZBkwV0UrxPGX9xt6Md4EWOlyQ62OFtY4qXDIzp+jmKF/sUMZjmUscHjs66aNtVujkKQClbvDB3ZuLaZc+HN1L3KdWa/H975vFz28O6oCo2DiMG94l9Tlzl+6AKj4RI/q3hVwuFd3tBks2HMSzYPPZRIM+HRvB28MVHZrXEv/IgZPXcOLiHTHSe8b7Q8X0UE4OSnw/eZgY0b5571mRNX1vRLfUqZmy2j7VslYqWwwa+jYgkYhplm7e1wd35Ur5YWjPFiKLSMHyH+v2iRH5lkaDu/YcOI733hiAcSP74emzEAQ8CxTLqI2zf1uN/334Jq4fXIGwyGhEREQiOibzUYz0BeCn39ZixidvYfDBFdBotPh3+0Fs33vc4m1njDGWexTMzXLWT2doSd+7RGNVdBGcjiiK3x1jxdyfrya4oJHaAdNcIqFOF/B6aaVimqYNDnFQZTIan+pUl8X4YJRbGP520keK01TuaJnsgP+UCXgsU4uAd2CiM5qoHfCLU0zqSPrbcrX4Z84DmRpbHRNQUmG+xI3ZPwn8GvKkW3aIBkCpNRoxh2f3NvXx5afv4NufV2DrnrwNyLImGgHp5+aGkJi8d/VYoluipKcnAiIjuesnn/A+zl+8f21/H2+KLIJ+Sfprv29xT8YHJZMwN0CJ6MREvO4RbvErIZEuiY74LM5dTMdEXeu3ZcniKkV0RaT03op3FXOG9vYIxfZMMqAj410yBKCdkhwxMc4NDdQK+GplYm7PK/Ik/Omowt80kf0LMrt0Lfj5TjH4yDOaj+FCfJ6w/nw3LFeG9WmN8WOGicFFNEXTlt1H7Tr4ZIwxBnGVos8zuVb6i6S//KU5exwSxL/soKDUXGBqjIJOQ+BpsE+ZIP7llsTvsbhVgqdeKsw4ALVTf6zbK/4Zo9H4v333PmrXrG5yf0JSMiZMm4szFw0TYjDGGLNFT6QaPJZxto8VfhyAFiIxqgQMf/8HazeDMcZYLq11UAE84xB7CWR+GQbGGGOMFaiH6ebhZKyw4gCUMcYYszKaeJ6xlwkHoIwxxpiVRWUyzRFjhRUHoIwxxpiVs5+7FLkfNc6YPeIAlDHGGLPShPMBUjWeSTU4nIdpixizRzwKnjHGGLOCce4RGAf91f9cJEprN4exAsUZUMYYY4wxVqA4AGWMMcYYYwWKA1DGGGOMMVagOABljDHGGGMFigNQxhhjzNqkKWOCHd0BFx9rt4axfMcBKGOMMWZttXvrb1u9BYxaBfiUs3aLGMtXHIAyxhhj1iZT6G8d3AAnd6BsE2u3iLF8xQEoY4wxZkskUqBiS2u3grF8xQEoY4wxZmvcS1i7BYzlKw5AGWOMMVujdAScva3dCsbyDQegjDHGmK2h0fAl61i7FYzlG74WPGOMMftRqh7QeDjgVwVw9gQSY4GQO8DJpUDAlcyf5+AKvLkecPEGtnwG3DmY/dHptD2P4kBMCHB+PXDxn4yPq9YJaDJCP3o9KQ64dxQ4sgCIj8rd65Q7AJVaA3cP5e75jNk4zoAyxhizH15lAJ0WuLwJ2PcTcHaVft7MVxYB5Zpl/ryWbwEKx5xtq24/oNs04Lk/sH8O8Owq0OkjfaBpRFOnH9D7GyAhGjj4C3BlC1CtMzBkPiBT5vKFAihaLffPZczGcQaUMcaY/bi6Vf/P2MUNwFsbgUZDgYenMj6nSAWg3gDg5F9Aq7ezn4Fs9Q5w/xiwdar+PgosaYR68zeAy5sBdRx0Ujk0tM4nF4D176U9P+AqMHA2UKev+YxpdlDWlgJYTVLuns+YDeMMKGOMMfumTgTiIvRzaJrT4QN9V/bTy9lfZ+kG+i5+Cm6NXfwXUDqnTpOU5FVWX695a7/p4/yPA0kqoHpn5JrCCShaNffPZ6wwZ0DlchnGDumI4r6eSFJrEBMbj9XbjiE0PFos796mHprVrwI/bw/8tnYPLt98lPpcNxdHvDGwPYp4u0Ot1mDNtmO4+yhILBvZvy3KlCgCnU4HjUaLTXvP4Jb/M5NtVy1fApNG9cC//53C/pPXxH3vvNoZRbzSTkIli/pg0Zo9uHIrbbsGft7uGDWwHVydHRGfkIRlmw4jMCTiha8pq+3n1dwvJ6BcqaLoP2Y6LG3hdx+gdbP60Gm1gESCNZv+w8z5ayy+HcYYy3cUBNLk7U6eQM3ugG8l4OSyjI+r0gEoURtY8qq+jjO7DIFf8C3T+4NuAVqNvgb19l7opCkTyKsTMq4jOVH/OEgA6JBjNCF9+eb6rn/GChmLdMEfPXsT1+4+ET+3a1oTI/q1wZwl28XvN+8H4OzV+3i9f9sMz+vfuSn8n4Tgl+W7ULakL959tTOmzlkDrVaH9btOiqCQlC7ugw9G9cRH3y+HLuU97OigQP8uTXDtjn67Br+t2Zv6c9kSRTDx9R64ntK29Ib3bY2j527h5MU7aFCzPEb1b4uZize/8DVltX1b9sXsJQgN0xfEly1VFIc3LsK2vSdw7XbG4Jwxxmxan2/1wRlRJwGXNgInl2TsRm/3HnB+LRAdmLMAlOpKtWp9ZtUY3UcDi1yLiF8V0c/0Nakl6wLXdpjWqtKAJ+Lopq8PzY2yjYHjv+fuuYwV5gCUMpeGQI08eBKMzi3Tpo54GBCa6XMb1qqA6fPWip8fBYQiMiYOVcqVwC3/gNTgkzg5ZCzifrVXK+w8fBH1q2d+vdyWDavh9OW7IoOaHmVfy5bwxc9/7xS/X7j+AK/0bAlfb3eR6czqNWV3+3nVqE4VLPx+MpQKBRRyOU6eu4Kxn8wSQbibqzPWLfoS5UqXgCouHg+fPIOHmwu6DJuc6foMwSfxdHOhJCik9J8ZCqkUSnnBlgg7pGzPcGtNcqm+OsWJ9r1MZu3mFEq8j/OXPexfHWUGDZegfBFNksgjpv564g/g0j/QuvpBXb0bpAoH/L+9+wBvqur/AP5N925poYyyyqaMMmXvISBD2VOQIYqgyNK/gr6CvoIooDKEFwQFFJA9ZW/KsAhSGaUUWtrSlu490vb/nBMa2qYzCWlpv5/nyZP03pPcm19P01/OumYWllCkPi+T0nYClMamsLz+OxRmZkgzMUHSs88YE7P8Jwclm1tBmaaEdS7lEtJTYWRmKWNrnBwD0/tnkNqoH8yiHsP4wTlk2FRAStcPkJ6WKt+fpZUtjNJzaSHNQvHs96Wx3aY8LM3Msr33suJlqMNlOcbxKbqNTdb7f/ru7Zrg5t1HBZaztjSHsbERYuIS1dvCo2Lh6GCj/vmNXq/IJNXKwgw/bTuubv0UrZWia150q+eVAJqaGKN1k9pYuiHHYPVnytnbIDouQba2ZoqIjoOjvY1GV3vO91SY4wvmZqaYO3lArvvE+xYtv/m5/ygQPYa9j9j4JFhamOPivjUYPag7tu49he8WTIOxkQKNu4+DtZUlLuxdjZDQMBTk81njMWJgL9jYWGPdrzvxTx6/qzoulZCUkIziYG9piZLC0dq6uE+h1GOMy258Eys1QXC/xYUq67JrKsyiA55vSA5R3SLvISPQA4GDvgf6LoDz6a/l7lQbZwS2HAUnjzWwtTCRrZCJVlYIfvYZY22bx3jRZ8IU6Yg1NoVzLuX8TMxhoUhXx7bKlTV4am6FhM7vAeIm/sf5nEJGfCgSanZABXNjGCvyP15UHgmokYkZypevAuPkWJRVJbkOl+UYPwwPLzkJqBjvKcZVLtt0Ti+vJ8Z9iluDWi4Y0rsNvlm/Xyau/bo0x3dZusNz06JRLYSERyMoJEf3iY7vyc7GslDHF5JTUvHl6t1aH1skneu+mQvX6i4y4bW1sUaLJvVkAtrSvSFWbfxDJtCxcQk4dvYK3BvWLvA1v1j2i7y1a+mGdUs/xo6DZ+DtG6hRzicwuFhaQMU/hujERCQrlSjub4XiDzIiPh5KMWaW9I4xfrFehvimp92F2bH/FqpsZKg/FGJST158ziOh1RiEJCRDkZaC5HYzgLgwxPtcRjxU/1zTFaplmKJgjlhYQxEbAkUeYzNTooIBI2OEKI2hSIxSbxez3tPNbZEcFSxjK2IcFfUUir3zYGnrjAzbylDEBgOxIUgatlp24YeHq+Y25EeRx+8oXZmMsLCgMtsCWtLr8MvOpBhjrLcMQ3RRN3dzxfJNh5CamlZg+fjEZKSnp8uELrMV1MnBFhFRcRplRZe8hXkHuFR0lOXtba0wf9oQuU9MIHJvUAM21pbYd+Ka+jkdWtbHRc97eR4/MjoO9jZWMDJSqFtBReunaAXN7z2JiVGFOb4+WkC//+J9pKakolXfSUhOTsWRLUthkctwBCEjS0tuYXh43sbT8EgM7tMJi1erhkFklZqejlQdm9e1JZJPXZv2dWX2rCsiMTUVKWkF12cqOsb4xXop4psSDETtL1zRggooTOQSSQkKUyAlDrB2BhyqIvGt7Zqv1X226sEPPVUL2efmyR15l+BYG3jo8Xx7lfoyMVU+uSNjmy3G4QGqW+YSSmICkvfpQn2eWefxzz8jJgQJxfx5WFxeijr8kjMrxhjrJQHt2b6J7O5eselQtrGbBfH08kXn1m44eNpTTkJysLOG96MgmRSKZDSzK7ymSwU5ZjMsIgb+QWGYu2SL+jXEbPmA4PBss9DFOE4xvnP11qN5Hlt0a/s/CUMb97rqSUhRMfHqY+b1nsSko4KOr68WUFtba/j5B8rk071hLbjVrw3fR6qxqZ7/3MXwAd2x6Y+jsLK0QO+ubRD6NP/m8PYt3XDJ87Z83LBuDdSoWhl/e93X+vyIiAzOqpzmxCCR7NXrBsQEP993YS1gaZ+9XPnaQKepwJXNqpnlqYnPJyvZVQJES2fmlYv8PVWPmw3JnoCK9URTEgHfS/mfZ+dpMlGVE6B08TCXdU2JSgGdE1CRNA7r204mbrMm9pfblMp0LF6nmk0uuqs7t24oWwhdnB2R+ppSJmVxCUnYfewqJg7thoUzRyAtLQ0/7zwlWyNNTY0xYXBXWFqYyVbS5BQl1m47gYRCJrcdWtTH37cfIik5y2h0AE0b1IB7/RrYvE/Vnb51/3lMeKOr7GYXZTftOVOo92Qoi1ZswrpvPsKtU78gPCIK9x88n60+Z9EqbF/zBbxOb0ZCQiL8A4KRlpZ/t/XCeW+jkrMT0tPSkJ6RgbW/7sLRc54GeCdERHoyZLnqkphP/lUlm3YVgcb9VbPSD8x/Xi4wlzU/M1s7g28DPlmGilVuBIxcDVxcD1xa/3xt0QvrgF5zVTPuH14Bqrqrlnw6t0Y1q/1Z61Fa67GAkysQ9C+QkQbU6Qy4tgXO/wQEq1pStSIS4kdXtH8+UQmmgHNLLRYno+Im1ioVY0TF+E9rKwuc2L4Ch0+cx6Lvt+JlJWabigH/obGxJaIL3sXBAYFRUez6eUEY4xer1Ma3+RDVZS4da6gWnheJoEhGr27JPenMubi8SDRzXgs+c3vWBDSTuJJRK7GGaBU5rlMuRO+5PVuMH5drCGWbt1TXgRdXSgrzAa79DnifKvTbsm47Fs73jyC08xzE1+mm2ihadP83RLXuaBlUautwCWJWjDEu/vVuSCv2tjY4uX2FHK5gYmKCG153sXiVjl09REQlnbgyUc6rExWWuFzm0raF3555+U1xy4eR6KL3uQC9S4ots8knlX5MQF9S4ZHRaNb7LY3tc6aOwJvD+mps/3bNFvy664SBzo6IiHT2RD9X2CMqiZiAljLfrt0ub0RE9BITE6ReRKsqUQmR+8q3REREVHyS4ngNeCrVmIASERGVxBZQba8fT/QSYAJKRERU0kT6F/cZEL1QTECJiIhKEjHz/QHHf1LpxgSUiIiouKUmqO5Ft7u4+WW/tDNRacNZ8ERERMXt1kFx/WXg5HLgz29UV1QiKsXYAkpERFRipDP5pDKBCSgRERERGRQTUCIiIiIyKCagRERERGRQTECJiIiIyKCYgBIRERGRQTEBJSIiIiKDYgJKRERERAbFBJSIiIiIDIoJKBEREREZFBNQIiIiIjIoJqBEREREZFAKOLfMMOwhiYiIiKgsYwsoERERERkUE1AiIiIiMigmoERERERkUExAiYiIiMigTAx7OKIXx9zMFGuWzEPdWtWQlJSC8MgofPzVGjx6/ATLv/gArZo1lNsTEhPx2dL1uPnvffm8kYN6YsrYQajrWg0Ll/+M9Vv3q1/T0sIc330+A+6N6iI9IwOLf/wVh05c0mlfWYvvxzPGoV/39khOSYVSqcTilZtx1uNvuU+hUGDRvCno3rEVMjIyZOw3bj+k076yGOOPpo9D7y6vID09Xf688ued2Hf0vHzMOqx7fDPVca2Ko7+twJbdf+LzpevlNtZh/cR4+cKZ6Ny2GSIio+XP5y7fwKLlG+Vj1mH91OHxw/th4sj+UKalIT09A/3HzZafy8UVXyagVKps2fUnTl3wlI/fGvEavv18BoZO/gRHTnlgzsIfkZaWjp6dWmPd0o/Qpt9kWe6fOz6YOm8JZkwcpvF677z5BpJTlegwcCqqVamIQ1u+xaVrtxAZHav1vrIW3yvXb2PFuu1ISk6BW72a2L1hMZr3Go/EpGQMea0r6taqjo6D3oGdjRWObfseF/+6Be8H/lrve9lpE+M1v+zGkpWb5eNKzo44u3sNzl+5iYioGNZhPcRXMDExxtIF03HktEe212Md1l+MRT3O2gCQiXVY9/i+2rUNBvfrgv5vzkFsXAIcy9khVZlWrPFlFzyVGuKbXOYfpeB56x6qVXGWj4+dvSr/KIXrt+6hUgUnGBurqv9t70fweRggv8HlNPDVTtj8xxH5+HFQCC795YW+3dvptK+sxff0RU+ZfAp37vvJlh8nR3t1nH7bfVS23EXFxGH/sfN4vU9nnfaVxRjHxMarn2NtaQmFQtXCJrAO6x5f4cO3R+Lg8Yt46B+U7TVZh/UX47ywDuse33fHD8aytdtk8ilERMaoe0yKK75MQKnUmjx6II6euaKxfdLoATh14S/1H2p+XCqVR8CTUPXPAUEhcKlUQad9ZTm+YriDf2AwAoJUsRExyRqnx0GhWWKo3b6yGuNJowbg/N41OLptBeYtWoXwZ12ZrMO6x7d543po1bQBNvx+QKMs67D+6rAoe2LHD/jlh8/QqL6rejvrsO7xFV32Td3qYN+mJTiydZn8vCju+LILnkqlGZOGoWa1yhgxdVW27YP7dcXA3h3xxsT/K7ZzK6vx7fhKU8yaOgoj31lgwDMtOzEWyZG4iWEOP341W46zfdm7GktCfMU4t68/eRdT5iwupjMtG3V4ycpfEfI0Uo6H7dOtLbas/I/s2k1ITCqGMy998TUxNkZ1l4pym4OdDXZt+Bp+AcE4cf4aigtbQKnUEeNS+nVvh7HT/yPHGWYSf5Cz3hEJ0GcIi4gq1GsFBoehamVV94ZQtUpFBAY/1WlfWYxv25aNsfyLmRj/wUI88AtUbxcxyRon0ZX0PIba7SvrdVgMKQkODUe7Vk3kz6zDusW3RtVKslVn5/qvcOXwekweMxAjB/XCikUz5X7WYf3U4eDQCJl8Cn+evoy4+ATUrukif2Yd1j2+4n3vPXJOdruLseGidbRF0/rFGl8moFSqvD12kBxHJVrZso6LG9C7I+ZNH4cRU+cX6Q/k4PELGDesr3wsBlm3b9VYfjjqsq+sxbdNi0b48ctZeGvmlzI5ykqMqRs9+FUYGRnJb+UDe3fC/mezt7XdVxZjLLrXMomEqXGDWvD2VU1mYR3WLb53ffzQpPtYOZlD3MQkmW37jmPmghVyP+uwfupwZWcn9eMWTeqjnL2dnNktsA7rHt89R86iW4cW8rGFuZn8gnrb+2GxxpfXgqdSQ3yAeR7bJD+04hMS1QO2+4+bA79re/A0PDJbl+Twt+fLn4cP7IF5742V/wRSlUrEJyRhwvuL4HXPV3a/iaUtxNiZtPR0fLNqCw4cuyCfr+2+shbfC/vXwtbaEqFhkep9Mz5dJv+xi3++X370Nrp1aClbP2Q38m+qcXba7iuLMRZj5kT3mljmSqlMw6pNu2SCI7AO6x7frGa/Mwp2ttbqZZhYh/UT4+0/LUJ5JwfZQpeYlCKX9Ln01y1ZhnVY9/iK5ZuWLHgPzdzqQiR9h09cwtI1W4s1vkxAiYiIiMig2AVPRERERAbFBJSIiIiIDIoJKBEREREZFBNQIiIiIjIoJqBEREREZFBMQImIiIjIoJiAEhEREZFBMQElIiIiIoNiAkpEREREBsUElIiIiIgMigkoERERERkUE1AiIiIiMigmoERERERkUExAiYiI9GD5wpkIunFA3k7tXKmxX6FQ4IPJw3HpwDr4Xdsj74VpEwbj3J41cr82xg3tg2tHfoaZqYnGvsljBqrPSdwcHey0OgaRvmnWViIiIsqmS7vm+H3Nwjz3vz9/mbwPj4jG59+uR0xsvEaZCcP7Ye60MVi7eS9uez9CaFgEbKwtMW3CECxa/jMyMjKylZ81dSRmTR2FrkPeg8/DgGz7lv3nfQwb0B0TZn6JHftPYvY7ozFuaF9s+P1AtnJnLl7H9Mjv0K9HO/Tr0R6GVMOlAto1q4f6tSrDycEW8QnJ8A0Iwb4TfyE0PLrI5XLTt0tzvN6zNQJDIrBw5U6tjm9uZoLeHd3hWtUZNV0qwNrKApt2n4HH394ax6tXszJmTxqQ67ksXrsXDwNCC4xL9Srl5TnXqlYR4juH7+NQ7Dp6BQHB4VqdU1FiUpIwASUiIipAo/qu8n7+4rWIionT2H/G4zo6vOKOhKQk7D58JtfXGDGoJ85dvoFFyzdma6E0MTbG3iPnNMr/suMI3ntrKKaMGYSPvlyl3j5xZH+MfL0XFq/cjJPn/5Lb/jhwEm+PG6SRgPo8CpA31+qVDZ6AvtrJHXWqV4Knly8CQsJhb2OFrm0a4dN3B2PJur0ICo0sUrmcHOys0bdzMyQlp+p0fBsrC/Tv1hLhUbEICI5A/VpVCnxvJz1uwS/wabZtoRH5J8tCtcpOmDt5ICKj43DwtCeMFAp0aeOGOZMG4Ou1exASFq31ORUmJiUJE1AiIqICNKxbE9Gxcfh520Gtnm9uZgq3eq749qffsm0fMbAnjp29iuQUzYQhPDIaew6fxdDXumHJys2IiIpB25aN8fnsSTh4/CJ+WL9DXXb/sQsyWe3QuikuXvsHJcGJi7ew4Y9TSEtLV2/769YDfDZ9KPp0boafd54uUrmchvZpi4ePQ6EwUsiETdvjR8cmYO6SzYiJS0SNKuXxybuDC3xvPn7BuP7vwyJGBBjUozVSU5VYsm4f4hOT5bbLN+9j0cwReL3nK1i77bjW51SYmJQkHANKRERUAJE8et311eq5330+Aw+v7oaJiTE+nj5OjsXc/8tSVKtSUbasnr9yI8/nrtu6D5aW5nKcZ5WK5bH2m3myRXPmZyuylbt154FMUF/t1gYlhe/jkGzJnxAaESNbHiuVdyhyuazq1qiEFm6u2H7kks7HV6aly0RPmy8VRkZFG7dbp0Yl3PENVCefgji296MnaFK/uux61/acChOTkoQtoERERPkwNTFB7ZouuLrndq6TeGLi4qFUpuX5/N2HzyJVmYY3h/XFgiXrEBkdi4AnoWjVrIE6ecyL9wN/nLl0HRNGvIY+3drCxMQEEz/8CgmJSRplve4+QGv3htAHkVhZWpgVqmxCYjJyDF/Nl52NZZ7d6oUpJyZrjejfARc87yIopODX0fb4+Rn/RhdYmJvJBFe0hu46ehl+QWEFPk98CUlN1awrKalKmJoYo4qzY6HGkeo7JsWBCSgREVE+6tWuBjNTU4wf3k/ecuo4cCp8/YPyfL7oEu/Yxh3xCYmyCz9zstHcdmPkvX9gSL7HF5OWxASo8o72GDv9C/gFBOdazi8gBENfc4M+iLGTeU22yemT735DeJTmuNjctHGvg3L2Nth/ylPrcl1aN4STvQ1WnDxUqGNqc/y8iJZJz3994eX9GHEJSahcwQG9O7hjzuSB+OZ/+/D4yfOJRLkJCYuCazVnmTBm1gNjYyM52ShzDKc2dIlJcWECSkREVMD4T+GDBcsRHKqZYOSXfGZyq1sT9x74Z5vpXs7BTo4HzK01M6u6tarJe7/AEJz1+DvPctExcbK73tLCHIlJz7t4tSFmZC/fWLhkJrqQXcUVy9tjVP+OeOAfnO9s7vzKWVuaY0CPVjh05rpMAIuisMfPj+jWX7ft+ReGf+76ybGgn703FG/0egU//Hok3+efvXobYwZ2wpuvd8axCzdlItqvaws5QUowMzUu8jnpEpPixASUiIiogPGfIlEUM9VTlUqtX0PMlC+qTm3c8dmHE/HALxC1a7jI5aDySkIzlxHNuZyTNhKSUnDXNxD6Irq9Z4zri8SkFKzddiLPcyyo3KCereX4ydNX/n0hx9fG04gY3Lj7CM3dXLO1bObm3LU7sgW2d4emaN+ivtz2KCBUJqMiEU3KZTJaQbSNSXFjAkpERJQPt3o14R8UonXyaWdrDZfKFXD3vl+27ZFRMTA1NYG1laXsns+puktFrFkyD173fDFi6nxc2PeTXJIprwTU3s5GtqYmJadAV6JbWLSsFUZsfFK+SZeFuSlmvNlXjin9dv1+OcNbm3LOjnbo1KoBdhz2gIOtqsVQEGMnxfk6OdggMTlVjknV5vi6iIyOl+chJhEVtATSvhPXcPzCTVR2LofE5BQ5ZlOs2ymEPluGqbC0jUlJwASUiIiogC746//c0+n5wu372ZftEbPZMxPNO/cfZdtnZWmBjSvmy8lNkz78CrFxCfhlx2G5MH0d16oaC9Nnvs5938fQh9rVKuplDKiYdDN9bB9UdLLHik2H8ORplNblxPhIIyMjjOzfQd5y+u/s0Th56RZ2HPEo8vF1Vd7RVk4kym05rbxamB/4P+/Kb1DbBRHRcQgOK9r5aROTkoIJKBERUR4qODmgglM5dbKojUb1VIvY3/HOnmR63rwr793d6mgkoD9+NUvOvB825VM8eTbudNOOw7kuTJ+pcYPact1QfdDHGFDRHT1leA95xZ/VW4/KK/7oUi4wNAKrfzua69qaopVz++FLsju8qK9bFGJtzZzjLKtWcoR7/Rrwuv9YvRqAqakxHO1tZFlxBab8tGpcS05C+uOIR5FWE9AmJiUJE1AiIqJ8xm4KTuXsMbhfV439t70f4q5P9q71nBrWq4mgkDCNKyiJ2e8i8ezUthm27Tuh3j7n3dHo270d5i5aiWs37qi3R0TGyKssiYXpF//4q1zOKVOThrXlElFHz1yGPuhjDOiwPm3RrGFN3LzrB2srczkDPasrN32KVE4kcjfvaMa6R7sm8j7nvsK+riCukGRlYQb7Z93YTevXQLlnM9JPXfZSd6tPGdFDLqP04HEIYuMSZTe66AIXrZ97jl9Vv56ri7NsQT5wylNe8SjrWp2vdWuB2z6BiE9IkjPi2zevDy9vf3mcrApzTkWNSUnCBJSIiCif8Z+CuPSluOU049NlBSegdWtqtHBmEonn3HdHyzUlxdhNsdbnzCkjZHf71l2aLVvrtuzDqNd7YdywvtmuhDSgV0cEBIXiwtWScRUkoWplJ3nv3qCGvOWUmQAWttyLOr7Qq0NTlC9nq/65RSNXeVOVu69OQG/ceYQ2TeuiZ/smsDQ3Q2x8Iv6+/UgmmYVpaYyMiUd6egZ6d2wKCzNThEXFYt/Jazhx6ZbcnlVhz+llpYBzS/1NBSMiIiqjli+ciY6tm+LVUTOhTEtDTGx8gc+xtbGCx8H/4asVm/D7XtVlGIvKzNQEVw5vwMqNO7HhtwMaV+sRk5ymTRiMaROGoHHXMfKKSUTFjZfiJCIi0hMx293rzFbs3bikUOXF5KI1m3bj3fGD5ZhFbYwY1FNOVtr8h+YalKKlVJyPSD6JShK2gBIREemBWDC+UgVH+ViM77t+S/uZ8/oirh8vJjNl8vD0yveyoUSGwgSUiIiIiAyKXfBEREREZFBMQImIiIjIoJiAEhEREZFBMQElIiIiIoNiAkpEREREBsUElIiIiIgMigkoERERERkUE1AiIiIiMigmoERERERkUExAiYiIiAiG9P+P4AMRcm+4iQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x350 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Waterfall for a single prediction generated during backtesting\n",
    "# ==============================================================================\n",
    "predictions = predictions.astype(data['Temperature'].dtypes)  # Ensure that the types are the same\n",
    "iloc_predicted_date = predictions.index.get_loc('2014-12-16')\n",
    "shap_values_single = explainer(predictions.iloc[:, 2:])\n",
    "shap.plots.waterfall(shap_values_single[iloc_predicted_date], show=False)\n",
    "\n",
    "fig, ax = plt.gcf(), plt.gca()\n",
    "fig.set_size_inches(6, 3.5)\n",
    "ax_list = fig.axes\n",
    "ax = ax_list[0]\n",
    "ax.tick_params(labelsize=8)\n",
    "ax.set_title(\"Waterfall plot for a single backtesting prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permutation feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Permutation feature importance is a model inspection technique that measures the contribution of each feature to the statistical performance of a fitted model on a given tabular dataset. This technique is particularly useful for non-linear or opaque estimators, and involves randomly shuffling the values of a single feature and observing the resulting degradation of the model's score. By breaking the relationship between the feature and the target variable, we determine how much the model relies on that particular feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>mean_importance</th>\n",
       "      <th>std_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lag_1</td>\n",
       "      <td>0.617276</td>\n",
       "      <td>0.014583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Temperature</td>\n",
       "      <td>0.411240</td>\n",
       "      <td>0.014405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lag_7</td>\n",
       "      <td>0.196190</td>\n",
       "      <td>0.001865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lag_2</td>\n",
       "      <td>0.122398</td>\n",
       "      <td>0.007803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lag_6</td>\n",
       "      <td>0.083912</td>\n",
       "      <td>0.003637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lag_3</td>\n",
       "      <td>0.041294</td>\n",
       "      <td>0.002019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lag_5</td>\n",
       "      <td>0.030787</td>\n",
       "      <td>0.001079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lag_4</td>\n",
       "      <td>0.024816</td>\n",
       "      <td>0.001021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       feature  mean_importance  std_importance\n",
       "0        lag_1         0.617276        0.014583\n",
       "7  Temperature         0.411240        0.014405\n",
       "6        lag_7         0.196190        0.001865\n",
       "1        lag_2         0.122398        0.007803\n",
       "5        lag_6         0.083912        0.003637\n",
       "2        lag_3         0.041294        0.002019\n",
       "4        lag_5         0.030787        0.001079\n",
       "3        lag_4         0.024816        0.001021"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training matrices used by the forecaster to fit the internal estimator\n",
    "# ==============================================================================\n",
    "X_train, y_train = forecaster.create_train_X_y(\n",
    "                       y    = data_train['Demand'],\n",
    "                       exog = data_train['Temperature']\n",
    "                   )\n",
    "\n",
    "# Permutation importances\n",
    "# ==============================================================================\n",
    "r = permutation_importance(\n",
    "        estimator    = forecaster.estimator,\n",
    "        X            = X_train,\n",
    "        y            = y_train,\n",
    "        n_repeats    = 3,\n",
    "        max_samples  = 0.5,\n",
    "        random_state = 123\n",
    "    )\n",
    "\n",
    "importances = pd.DataFrame({\n",
    "                  'feature': X_train.columns,\n",
    "                  'mean_importance': r.importances_mean,\n",
    "                  'std_importance': r.importances_std\n",
    "              }).sort_values('mean_importance', ascending=False)\n",
    "importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partial dependence plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Partial dependence plots (PDPs) are a useful tool for understanding the relationship between a feature and the target outcome in a machine learning model. In scikit-learn, you can create partial dependence plots using the `plot_partial_dependence` function. This function visualizes the effect of one or two features on the predicted outcome, while marginalizing the effect of all other features.\n",
    "\n",
    "The resulting plots show how changes in the selected feature(s) affect the predicted outcome while holding other features constant on average. Remember that these plots should be interpreted in the context of your model and data. They provide insight into the relationship between specific features and the model's predictions.\n",
    "\n",
    "A more detailed description of the Partial Dependency Plot can be found in <a href=\"https://scikit-learn.org/stable/modules/partial_dependence.html#partial-dependence-and-individual-conditional-expectation-plots\">Scikitlearn&#39;s User Guides</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 900x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scikit-learn partial dependence plots\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(9, 4))\n",
    "ax.set_title(\"Decision Tree\")\n",
    "pd.plots = PartialDependenceDisplay.from_estimator(\n",
    "    estimator = forecaster.estimator,\n",
    "    X         = X_train,\n",
    "    features  = [\"Temperature\", \"lag_1\"],\n",
    "    kind      = 'both',\n",
    "    ax        = ax,\n",
    ")\n",
    "ax.set_title(\"Partial Dependence Plot\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
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